DETAILED ACTION
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 .
Election/Restrictions
Applicant’s election of Group I (claims 17-29) in the reply filed on 06/10/2026 is acknowledged. Because Applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election has been treated as an election without traverse. MPEP 818.01(a).
Status of Claims
Claims 17-32 are pending in the application with claims 30-32 withdrawn. Claims 17-29 are examined herein.
Claim Objections
Claims 17, 20-23, and 27 are objected to because of the following informalities:
Claim 17: “a unit allowing neutron poison to be injected” should be amended to recite “a neutron injection unit allowing neutron poison to be inject” for consistency with later recitations to a “neutron poison injection unit”
Claim 17: “a unit provided for injecting water” should be amended to recite “a water injection unit for injecting water into the primary circuit” for consistency with later recitations to a “water injection unit”
Claim 17: “steps S20/ and S30” should be amended to recite “steps S20/ and S30/”
Claims 20 and 21: “wherein the cost function convergence criterion” should be amended to recite “wherein meeting the cost function convergence criterion”
Claim 21: the term “and” should be added to the end of the second bullet point (“below a determined limit; and”)
Claims 22 and 27: “the primary liquid” should be amended to recite “the primaryheat transfer fluid”
Claim 23: “said power variation” should be amended to recite “said at least one power variation”
Claim 23: “the maximum possible” should be amended to recite “[[the]] a maximum possible”
Claim 27: “the sequence of injection” should be amended to recite “the sequence”
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 17-29 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 17 recites “acquiring a reactor power program to be supplied by the nuclear reactor”. It is unclear whether the claim is intending to recite the reactor power program is acquired from the nuclear reactor (e.g., the nuclear reactor supplies the reactor power program for the “acquiring” step), or something else.
Claim 17 is indefinite because it is unclear which steps are considered “sub-steps”.
Claim 17 recites “at least one magnitude characteristic”. It is unclear what constitutes a “magnitude characteristic”. This further renders unclear the relationship between the previously recited “plurality of operating parameters”, “at least one parameter”, and “one parameter”.
Claim 17 recites “the aid”. There is insufficient antecedent basis for this phrase in the claim. Perhaps the claim should be amended to recite “the evolution being calculated using a predictive model of the core of the reactor”.
Claim 17 recites “evaluating a cost function, using the calculated evolution”. It is unclear what the “cost function” is a cost function of and/or what the “cost function” is applied to.
Claim 17 recites “iteratively, implementing the following sub-steps”, “sub-steps S31/ to S33/ being repeated until a cost function convergence criterion is met”, and “steps S20/ and S30 being repeated with a time period less than 60 minutes”. It is unclear which steps are being repeated under what conditions. For example, are the sub-steps iteratively implemented until the cost function convergence criterion is met? Are the sub-steps iteratively implemented using the same current values? It is also unclear the relationship between the “given time interval” and the “time period”. It is unclear if the claim is intending to recite the given time interval in steps S20/ and S30/ is less than 60 minutes, steps S20/ and S30/ are repeated every 60 minutes or less, Steps S20/ and S30/ are repeated and carried out within less than 60 minutes, or something else.
Claim 17 recites “communicating to an operator an optimum injection sequence allowing the cost function convergence criterion to be met”. It is unclear the relationship between the “optimum injection sequence” and the previously recited “injection sequence of neutron poison and/or water”. This further renders the phrase “the operator controlling the neutron poison and water injection units as a function of the optimum injection sequence” unclear. The claim previously recites “generating an injection sequence of neutron poison and/or water”. Thus, the claim allows for generating an injection sequence of neutron poison only, an injection sequence of water only, or an injection sequence of neutron poison and an injection sequence of water. However, the recitation of “controlling the neutron poison and water injection units” appears to require an injection sequence of neutron poison and an injection sequence of water. It is therefore unclear if the claim is intending to recite “controlling the neutron poison and/or water injection units as a function of the optimum injection sequence”, “generating an injection sequence of neutron poison and water”, or something else. The claim further appears to be missing a step of, for example, selecting and/or determining the “optimum injection sequence”. Additionally, it is unclear if the phrase “the operator controlling the neutron poison and water injection units” is intending to be an additional step of the claimed method. Perhaps the claim should be amended to recite a step of “S50/ controlling the neutron poison and water injection units as a function of the optimum injection sequence”.
Claim 22 recites “wherein in sub-step S31/ the neutron poison and/or water injection sequence into the primary liquid is generated considering results obtained in the previous iteration, by a gradient descent algorithm”. It is unclear what the “results” are referring to and the relationship between the “results” and the previously recited “generat[ed] injection sequence”, “calculat[ed] evolution”, and “evaluat[ed] cost function” in parent claim 17. Additionally, it is unclear what “results” are considered in the first iteration given that there is no “previous iteration”. Further, in view of the above, it is unclear which “iteration” the claim is intending to refer to (e.g., iterative implementation of the sub-steps, repeating of S31/ to S33/ until the cost function convergence criterion is met, repeating of S20/ and S30/ with a time period less than 60 minutes). It is also unclear what feature is “by a gradient descent algorithm”.
Claim 23 recites “wherein step S30/ comprises a sub-step S35/ for determining an optimum slope for an evolution of the power as a function of time during the power evolution from the first power to the second power”. It is unclear the relationship between the “evolution of the power” and the “evolution of at least one magnitude characteristic of a state of the core” previously recited in parent claim 17. The specification discloses the “at least one magnitude characteristic of a state of the core” may include a magnitude of the power supplied by the core ([00114]). Thus, “an evolution of the power” would appear to also be an evolution of at least one magnitude characteristic of a state of the core. It is further unclear which of the previously recited “powers” the “power” is intending to refer to. There is also insufficient antecedent basis for the phrase “the power evolution”. It is unclear if the claim is intending to refer to the previously recited “at least one power variation”. Additionally, because the claims do not clearly recite the progression of the steps employed, it is unclear whether sub-step S35/ is also part of the “sub-steps ... being repeated until a cost function convergence criterion is met”.
Claim 23 recites “calculating an evolution of the at least one magnitude characteristic of the state of the core of the nuclear reactor during said power variation with the aid of the predictive model of the core of the reactor, for several values of slope, the injection of neutron poison or water per unit of time being considered constantly equal to the maximum possible”. It is unclear the relationship between the “calculating an evolution of at least one magnitude characteristic of the state of the core” and the “calculating an evolution of at least one magnitude characteristic of a state of the core” previously recited in parent claim 17. It is further unclear if the “at least one magnitude characteristic” is referring to the same characteristic recited in parent claim 17 or a different characteristic. It is further unclear what the “slope” is a slope of and the relationship between the “several values of slope” and the previously recited “optimum slope”. It is similarly unclear the relationship between the “slope value minimizing the cost function” and the previously recited “several values of slope” and “optimum slope”. It is also unclear the relationship between the “injection of neutron poison or water per unit of time” and the previously recited “injection sequence of neutron poison and/or water” and “optimum injection sequence” in parent claim 17. It is further unclear what the “maximum possible” is intending to refer to in the claim.
Claim 23 recites “evaluating the cost function, using the variation calculated for each slope value”. It is unclear what the “cost function” is a cost function of and/or what the “cost function” is applied to. It is further unclear the relationship between step S352/ and Step S33/ previously recited in parent claim 17. This further renders unclear the relationship between the step of “selecting the slope value minimizing the cost function” and “an optimum injection sequence allowing the cost function convergence criterion to be met”. There is also insufficient antecedent basis for the phrase “the variation calculated for each slope value”.
Claim 27 recites “wherein the sequence of injection of neutron poison and/or water into the primary liquid comprises a plurality of injection operations, each operation being characterized by an operation quantity and duration, a number of operations in the injection sequence being between 2 and 12, the duration of the operation being between 2 minutes and 60 minutes”. It is unclear which of the previously recited “injection sequence[s]” the claim is intending to refer to. It is further unclear what is meant by an “operation quantity”. It is further unclear which operation “the operation” in line 4 is intending to refer to. For example, it is unclear if the claim is intending to recite “the duration of each operation”. This further renders unclear which operation the “one operation” in claim 28 is intending to refer to.
The term “substantially” in claim 28 is a relative term which renders the claim indefinite. The term is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Any claim not explicitly addressed above is rejected because it is dependent on a rejected base claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 17-21, 24, and 27-29, as best understood, are rejected under 35 U.S.C. 103 as being unpatentable over “Design of a Load Following Controller for APR+ Nuclear Plants” (“Lee”).
Regarding claim 17, Lee (newly cited) (see FIGS. 1, 4, 7) discloses a method for controlling a nuclear reactor, the nuclear reactor having a core comprising a plurality of nuclear fuel assemblies, a primary circuit for cooling the core in which a primary heat transfer fluid (“coolant”) containing a neutron poison circulates, a neutron injection unit allowing neutron poison to be injected into the primary heat transfer fluid, and a water injection unit provided for injecting water into the primary circuit (Abstract, p. 370: “Many power reactors can be controlled in part by varying the boric acid (H3BO3) concentration in the reactor coolant”; p. 375: “The thermal power is regulated by five regulating control rod banks as well as by changing the concentration of boric acid”), the method comprising the following steps:
S10/ acquiring a reactor power program (“reference trajectory”, “desired power”, “w”) to be supplied by the nuclear reactor, this program comprising at least one reactor power variation from a first power to a second power (p. 371: “w is a setpoint (desired reactor power and ASI) or reference sequence”; see variations in power of the “desired power” in FIG. 7);
S20/ acquiring current values of a plurality of operating parameters of the nuclear reactor, comprising at least one parameter (“power”) characterizing a core power supplied by the core of the reactor and one parameter (“ASI”) characterizing a neutron flux distribution in the core (p. 371: “The purpose of taking new measurements at each time step is to compensate for any unmeasured disturbances and model inaccuracies”);
S30/ iteratively, implementing the following sub-steps:
S31/ generating an injection sequence (“future control moves”, “control input”, “u”, “s”, “c”) of neutron poison and/or water into the primary heat transfer fluid covering a given time interval (p. 371: “At every time point, model predictive control requires a non-linear solution to an optimization problem to calculate the optimal control inputs over a fixed number of future time points, which are known as the time horizon. The basic idea of model predictive control is to calculate a sequence of future control signals”; p. 377: “The boron concentration is adjusted as follows” – note that the boron concentration is adjusted as a function of u(t), w(t), and y(t)),
S32/ calculating an evolution of at least one magnitude characteristic (“power”, “ASI”, “ŷ”) of a state of the core of the nuclear reactor during said given time interval using the acquired reactor power program, the acquired current values of the operating parameters and the generated injection sequence, the evolution being calculated with the aid of a predictive model (“SVR model”) of the core of the reactor (p. 370: “The SVR is used to predict the future output required in an optimization problem. That is, the behavior of the process over a predicted horizon at the present time is considered and the process output to changes in the manipulated variable is predicted using the SVR model”, “Based on an SVR reactor model consisting of the control rod position, the past nuclear reactor power and ASI, the future nuclear reactor power and ASI were predicted using the SVR model”, “For any assumed set of present and future control moves, the future behavior of the process outputs can be predicted over a predicted horizon, N, and the M present and future control moves ... are calculated”; p. 375: “The block ‘Optimization by GA’ calculates the optimal control inputs of the R5 control rod position and the part-strength control rod position minimizing the cost function of Eq. (1) using the genetic algorithm.... The block ‘Output prediction by an SVR model’ predicts the future outputs of the power level and the ASI by using the SVR model”);
S33/ evaluating a cost function (“J”, Eq. (1) (p. 371)), using the calculated evolution (p. 370: “the M present and future control moves ... are calculated to minimize the quadratic objective function”; p. 371: “The basic idea of model predictive control is to calculate a sequence of future control signals in such a way that it minimizes the multistage cost function defined over a prediction horizon”);
sub-steps S31/ to S33/ being repeated until a cost function convergence criterion is met (p. 370: “The model predictive control method aims to solve an optimization problem for a finite future at the current time and to implement the first optimal control input as the current control input. The procedure is then repeated at each subsequent time step”; p. 371: “At the next time step, new values of the measured output are obtained, the control horizon shifts forward by a single step, and the same calculations are repeated”, “The basic idea of model predictive control is to calculate a sequence of future control signals in such a way that it minimizes the multistage cost function defined over a prediction horizon”; p. 376: “The optimal control input can be obtained by solving the minimization objective function of Eq. (1) using a genetic algorithm”);
S40/ communicating to an operator an optimum injection sequence allowing the cost function convergence criterion to be met, the operator controlling the neutron poison and water injection units as a function of the optimum injection sequence (pp. 369-370: “The basic concept of model predictive control is to solve an optimization problem for a finite future at the current time, and once a future input trajectory has been chosen, only the first element of that trajectory is used as input to the plant”; p. 370: “The moves of the manipulated variables are selected so that the predicted output has certain desirable characteristics”; pp. 370-371: “Although M control moves are calculated, only the first control move is implemented”);
steps S20/ and S30/ being repeated with a time period less than 60 minutes (p. 376: “The sampling time was 5 sec”).
Although Lee does not explicitly disclose a plurality of fuel assemblies, Lee discloses the nuclear reactor is an APR nuclear reactor (Abstract). APRs are pressurized water nuclear reactors. All operational pressurized water nuclear reactors, including APRs, comprise a plurality of nuclear fuel assemblies1,2. Thus, Lee, which discloses a pressurized water reactor, also discloses a plurality of fuel assemblies.
Regarding claim 18, Lee discloses the control method according to claim 17. Lee discloses the at least one magnitude characteristic of the state of the core calculated in step S30/ comprises said parameter characterizing the neutron flux distribution in the core (FIG. 7, p. 370: “Based on an SVR reactor model consisting of the control rod position, the past nuclear reactor power and ASI, the future nuclear reactor power and ASI were predicted using the SVR model”; p. 375: “The block ‘Optimization by GA’ calculates the optimal control inputs of the R5 control rod position and the part-strength control rod position minimizing the cost function of Eq. (1) using the genetic algorithm.... The block ‘Output prediction by an SVR model’ predicts the future outputs of the power level and the ASI by using the SVR model”).
Regarding claim 19, Lee discloses the control method according to claim 18. Lee discloses the cost function characterizes an evolution of a deviation between said parameter characterizing the neutron flux distribution in the core and a reference value over said given time interval (Eq. (1) (p. 371)).
Regarding claim 20, Lee discloses the control method according to claim 17. Lee discloses the cost function convergence criterion comprises reaching an extremum of the cost function (p. 370: “The basic idea of model predictive control is to calculate a sequence of future control signals in such a way that it minimizes the multistage cost function defined over a prediction horizon”; p. 376: “The optimal control input can be obtained by solving the minimization objective function of Eq. (1) using a genetic algorithm”).
Regarding claim 21, Lee discloses the control method according to claim 17. Lee discloses the cost function convergence criterion comprises meeting at least one constraint selected from the following list: a deviation between said parameter characterizing the neutron flux distribution in the core and a reference value during said given time interval remains constantly below a determined limit; a quantity of neutron poison injected per unit of time during said given time interval remains below a determined limit; and a quantity of water injected per unit of time during said given time interval remains below a determined limit (Eq. (1) (p. 371), p. 376).
Regarding claim 24, Lee discloses the control method according to claim 17. Lee discloses the predictive model of the core of the reactor is non-linear (p. 371: “At every time point, model predictive control requires a non-linear solution to an optimization problem”).
Regarding claim 27, Lee discloses the control method according to claim 17. Lee discloses the sequence of injection of neutron poison and/or water into the primary heat transfer fluid comprises a plurality of injection operations, each operation being characterized by an operation quantity and duration, a number of operations in the injection sequence being between 2 and 12, the duration of the operation being between 2 minutes and 60 minutes (FIGS. 1, 7).
Regarding claim 28, Lee discloses the control method according to claim 27. Lee discloses the time period is less than or equal to the duration of one operation in the injection sequence (FIGS. 1, 7, p. 376: “The sampling time was 5 sec”).
.
Regarding claim 29, Lee discloses the control method according to claim 17. Lee discloses the given time interval has a total duration of between 10 minutes and a duration of the reactor power program (FIGS. 1, 7).
Claim 22, as best understood, is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of “Core Power Receding Horizon Control (RHC) Approach for Pressurized Water Reactor (PWR) Nuclear Power Plants” (“Shimmari”).
Regarding claim 22, Lee discloses the control method according to claim 17. Lee discloses in sub-step S31/ the neutron poison and/or water injection sequence into the primary heat transfer fluid is generated considering results obtained in the previous iteration (p. 371: “To obtain the control inputs, the predicted outputs first need to be calculated as a function of previous inputs and outputs as well as future control signals”), but does not appear to explicitly disclose a gradient descent algorithm.
Shimmari (newly cited) (see FIGS. 1-4) is similarly directed towards a method for controlling a nuclear reactor comprising iteratively generating a control sequence and evaluating the control sequence using a cost function (Eq. (58) (p. 2193), p. 2189: “RHC makes use of explicit dynamic plant model to predict the effect of future reactions of the manipulated variables on the output and the control signal obtained by minimizing the cost function”; p. 2190: “If a reasonably accurate dynamic model of the process is available, model and current measurements can be used to predict future values of the outputs”, “A process model is used to predict the current values of the output variables”, “The objective of the RHC control calculations is to determine a sequence of control moves ... so that the predicted response moves to the set point in an optimal manner.... RHC strategy calculates a set of M values of the input {u (k+i-1), i=1, 2,..., M}. The set consists of the current input u(k) and M-1 future inputs.... The inputs are calculated so that a set of P predicted outputs y(k+i), i=1, 2,..., P} reaches the set point in an optimal manner”; p. 2191: “A quadradic program (QP) is an optimization problem with a quadratic objective function and linear constraints”). Shimmari teaches evaluating the control sequence using a gradient descent algorithm (p. 2191: “Each iteration of the gradient projection algorithm consists of two stages. The first stage involves searching along the steepest descent direction from the current point x”). Shimmari further teaches the gradient descent algorithm provides the advantages of allowing the active set to change rapidly from iteration to iteration (p. 2191: “The gradient projection method allows the active set to change rapidly from iteration to iteration”). It would have therefore been obvious to a person having ordinary skill in the art (“POSA”) to use a gradient descent algorithm, as taught by Shimmari, in Lee’s method because Shimmari teaches this as a suitable method of determining an optimum control sequence the provides the predictable advantages of allowing for rapid changes across iterations (Shimmari, p. 2191).
Claims 25-26, as best understood, are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of “Extension of Load Follow Capability of a PWR Reactor by Optimal Control” (“Winokur”).
Regarding claim 25, Lee discloses the control method according to claim 24, but does not appear to disclose the predictive model of the core comprises several sub-models as recited in claim 25.
Winokur (newly cited) is similarly directed towards a method for controlling a nuclear reactor comprising calculating an evolution of at least one magnitude characteristic of a state of a core of the nuclear reactor using a non-linear predictive model of the core (Abstract). Winokur teaches the predictive model comprises several sub-models (Abstract), each sub-model modeling a level (“node”) of the core of the nuclear reactor and comprising at least one equation describing a kinetic of a neutron density at said level and an equation describing a temperature of a primary heat transfer fluid at said level (Eqs. (2)-(3) (p. 933), Table 1 (p. 936)), the model further comprising equations describing neutron exchanges between the levels and equations characterizing a reactivity at each level (Eq. (6) (p. 933), p. 933: “The basic assumptions are that the external source of neutrons to a given node, from another node, is proportional to the average flux in that node, and that the coupling effect is the same in both directions”). Winokur further teaches the non-linear predictive sub-model approach has the advantages of directly calculating optimal control using presently available means of reactor control and providing more detailed information on the axial flux distribution (p. 932: “The algorithms used in the [Differential Dynamic Programming] DPP method converge for a broad spectrum of load changes.... As a consequence, three main advantages are obtained: direct calculation of the optimal control law using presently available means of reactor control, reduction in computing time, and simplified implementation”, “a two-node reactor dynamics model was used, enabling the treatment of spatial control problems arising from load following, yet with computing requirements small enough to allow a fast and efficient search of the optimal control”; p. 938: “A multinode model could be used to obtain more detailed information on the axial flux distribution, provided the coupling coefficients are properly defined and computed”). It would have therefore been obvious to a POSA to use Winokur’s model in Lee’s control method for the benefits thereof. Thus, modification of Lee for the predictable purpose of providing direct and detailed calculations, as suggested by Winokur, would have been obvious to a POSA.
Regarding claim 26, Lee in view of Winokur teaches the control method according to claim 25. Winokur teaches the equations characterizing reactivity at each level take into account one or more of the following effects: effect due to a variation in the temperature of the primary heat transfer fluid at said level; effect due to a variation in the power supplied by the core at said level; effect due to displacement of groups of control rods; effect due to a variation in a concentration of neutron poison in the primary heat transfer fluid; and effect due to a variation in a concentration of xenon in the nuclear fuel assemblies at said level (Eqs. (6)-(7) (p. 933), p. 933: “The average absorption cross section ... is a function of core composition and burnup, and also depends directly on core controls and Xe concentration, thus being a function of the load follow maneuvers”). Thus, Lee, modified to include Winokur’s non-linear predictive sub-model approach, would have resulted in the features of claim 26.
Claims 17-21, 24, and 27-29, as best understood, are rejected under 35 U.S.C. 103 as being unpatentable over WO Publication No. 2019/149907 (“Grossetete”).
Citations to Grossetete refer to the corresponding US Publication No. 2022/0084706.
Regarding claims 17 and 20, Grossetete (cited via Applicant-submitted IDS) (see FIGS. 1-2, 9) discloses a method for controlling a nuclear reactor (1), the nuclear reactor having a core (5) comprising a plurality of nuclear fuel assemblies ([0111]), a primary circuit (10) for cooling the core in which a primary heat transfer fluid (“coolant”) containing a neutron poison circulates ([0113]-[0114]), a unit (15) allowing neutron poison to be injected into the primary heat transfer fluid, and a unit (15) provided for injecting water into the primary circuit ([0114], [0154], [0158]), the method comprising the following steps:
S10/ acquiring a reactor power program (“DU”) to be supplied by the nuclear reactor, this program comprising at least one reactor power variation from a first power to a second power ([0170]-[0173], [0184]-[0185], [0209], [0216], [0241], [0244], [0251], [0282]);
S20/ acquiring current values (“Y”) of a plurality of operating parameters of the nuclear reactor, comprising at least one parameter (“PK”) characterizing a core power supplied by the core of the reactor and one parameter (“AO”) characterizing a neutron flux distribution in the core ([0162], [0179], [0183], [0186], [0245], [0405]);
S30/ iteratively, implementing the following sub-steps:
S31/ generating an injection sequence (“US”) of neutron poison and/or water into the primary heat transfer fluid covering a given time interval ([0247], [0374], [0381]-[0385], [0407]),
S32/ calculating an evolution of at least one magnitude characteristic (“AO”) of a state of the core of the nuclear reactor during said given time interval using the acquired reactor power program, the acquired current values of the operating parameters and the generated injection sequence, the evolution being calculated with the aid of a predictive model of the core of the reactor ([0179], [0241], [0304], [0374], [0403]);
S33/ evaluating a cost function, using the calculated evolution ([0384]-[0386]);
S40/ communicating to an operator an optimum injection sequence allowing a cost function convergence criterion to be met, the operator controlling the neutron poison and water injection units as a function of the optimum injection sequence ([0188]-[0189], [0195], [0250], [0259], [0381], [0403]);
steps S20/ and S30 being repeated with a time period less than 60 minutes ([0377]-[0384]).
Grossetete does not appear to explicitly disclose repeating the generating, calculating, and evaluating steps until a cost function convergence criterion is met. However, Grossetete discloses using an interior point optimization method implemented by the function fmincon in Matlab to evaluate an objective function given defined constraints to determine the optimum injection sequence ([0392]-[0403]). As best understood by Examiner, the interior point fmincon algorithm is an optimization method which evaluates a cost function at each iteration starting from an initial value (e.g., Grossetete’s “initial state vector” [0406]-[0407]) and repeats until the cost function is minimized3,4,5. Thus, as Grossetete discloses determining the optimum injection sequence using the interior point fmincon algorithm, Grossetete discloses iteratively repeating the generating, calculating, and evaluating steps until a cost function convergence criterion is met.
Regarding claim 18, Grossetete discloses the control method according to claim 17. Grossetete discloses the at least one magnitude characteristic of the state of the core calculated in step S30/ comprises said parameter characterizing the neutron flux distribution in the core ([0159], [0162], [0179], [0374], [0405]).
Regarding claim 19, Grossetete discloses the control method according to claim 18. Grossetete discloses the cost function characterizes an evolution of a deviation between said parameter characterizing the neutron flux distribution in the core and a reference value over said given time interval ([0386], [0391]-[0396]).
Regarding claim 21, Grossetete discloses the control method according to claim 17. Grossetete discloses the cost function convergence criterion comprises meeting at least one constraint selected from the following list: a deviation between said parameter characterizing the neutron flux distribution in the core and a reference value during said given time interval remains constantly below a determined limit; a quantity of neutron poison injected per unit of time during said given time interval remains below a determined limit; a quantity of water injected per unit of time during said given time interval remains below a determined limit ([0401]).
Regarding claim 24, Grossetete discloses the control method according to claim 17. Grossetete discloses the predictive model of the core of the reactor is non-linear ([0270]-[0272], [0403]).
Regarding claim 27, Grossetete discloses the control method according to claim 17. Grossetete discloses the sequence of injection of neutron poison and/or water into the primary heat transfer fluid comprises a plurality of injection operations, each operation being characterized by an operation quantity and duration, a number of operations in the injection sequence being between 2 and 12, the duration of the operation being between 2 minutes and 60 minutes (FIG. 18, [0377]-[0380]).
Regarding claim 28, Grossetete discloses the control method according to claim 27. Grossetete discloses the time period is less than or substantially equal to the duration of one operation in the injection sequence (FIG. 18, [0279], [0377]-[0380]).
Regarding claim 29, Grossetete discloses the control method according to claim 17. Grossetete discloses the given time interval has a total duration of between 10 minutes and a duration of the reactor power program (FIG. 18, [0377]-[0380]).
Claim 22, as best understood, is rejected under 35 U.S.C. 103 as being unpatentable over Grossetete in view of Shimmari.
Regarding claim 22, Grossetete discloses the control method according to claim 17. Grossetete discloses in sub-step S31/ the neutron poison and/or water injection sequence into the primary heat transfer fluid is generated considering results obtained in the previous iteration ([0409]), but does not appear to explicitly disclose a gradient descent algorithm.
Shimmari (see FIGS. 1-4) is similarly directed towards a method for controlling a nuclear reactor comprising iteratively generating a control sequence and evaluating the control sequence using a cost function (Eq. (58) (p. 2193), p. 2189: “RHC makes use of explicit dynamic plant model to predict the effect of future reactions of the manipulated variables on the output and the control signal obtained by minimizing the cost function”; p. 2190: “If a reasonably accurate dynamic model of the process is available, model and current measurements can be used to predict future values of the outputs”, “A process model is used to predict the current values of the output variables”, “The objective of the RHC control calculations is to determine a sequence of control moves ... so that the predicted response moves to the set point in an optimal manner.... RHC strategy calculates a set of M values of the input {u (k+i-1), i=1, 2,..., M}. The set consists of the current input u(k) and M-1 future inputs.... The inputs are calculated so that a set of P predicted outputs y(k+i), i=1, 2,..., P} reaches the set point in an optimal manner”; p. 2191: “A quadradic program (QP) is an optimization problem with a quadratic objective function and linear constraints”). Shimmari teaches evaluating the control sequence using a gradient descent algorithm (p. 2191: “Each iteration of the gradient projection algorithm consists of two stages. The first stage involves searching along the steepest descent direction from the current point x”). Shimmari further teaches the gradient descent algorithm provides the advantages of allowing the active set to change rapidly from iteration to iteration (p. 2191: “The gradient projection method allows the active set to change rapidly from iteration to iteration”). It would have therefore been obvious to a POSA to use a gradient descent algorithm, as taught by Shimmari, in Grossetete’s method because Shimmari teaches this as a suitable method of determining an optimum control sequence the provides the predictable advantages of allowing for rapid changes across iterations (Shimmari, p. 2191).
Claims 25-26, as best understood, are rejected under 35 U.S.C. 103 as being unpatentable over Grossetete in view of Winokur.
Regarding claim 25, Grossetete discloses the control method according to claim 24. Grossetete discloses the predictive model of the core of the reactor comprises at least one equation describing a kinetic of a neutron density and an equation describing a temperature of the primary heat transfer fluid, and equations characterizing a reactivity ([0304]), but does not appear to disclose the predictive model of the core comprises several sub-models.
Winokur is similarly directed towards a method for controlling a nuclear reactor comprising calculating an evolution of at least one magnitude characteristic of a state of a core of the nuclear reactor using a non-linear predictive model of the core (Abstract). Winokur teaches the predictive model comprises several sub-models (Abstract), each sub-model modeling a level (“node”) of the core of the nuclear reactor, the model further comprising equations describing neutron exchanges between the levels (Eq. (6) (p. 933), p. 933: “The basic assumptions are that the external source of neutrons to a given node, from another node, is proportional to the average flux in that node, and that the coupling effect is the same in both directions”). Winokur further teaches the sub-model approach has the advantages of providing more detailed information on the axial flux distribution (p. 932: “a two-node reactor dynamics model was used, enabling the treatment of spatial control problems arising from load following, yet with computing requirements small enough to allow a fast and efficient search of the optimal control”; p. 938: “A multinode model could be used to obtain more detailed information on the axial flux distribution, provided the coupling coefficients are properly defined and computed”). It would have therefore been obvious to a POSA to use Winokur’s sub-model approach in Grossetete’s control method for the benefits thereof. Thus, modification of Grossetete for the predictable purpose of providing detailed calculations, as suggested by Winokur, would have been obvious to a POSA.
Regarding claim 26, Grossetete in view of Winokur teaches the control method as claimed in claim 25. Grossetete discloses the equations characterizing reactivity at each level take into account one or more of the following effects: effect due to a variation in the temperature of the primary heat transfer fluid at said level; effect due to a variation in the power supplied by the core at said level; effect due to displacement of groups of control rods; effect due to a variation in a concentration of neutron poison in the primary heat transfer fluid; and effect due to a variation in a concentration of xenon in the nuclear fuel assemblies at said level ([0304]).
Note on Claim Interpretation
It should be noted, as stated in MPEP 2173.06, “where there is a great deal of confusion and uncertainty as to the proper interpretation of the limitations of a claim, it would not be proper to reject such a claim on the basis of prior art. As stated in In re Steele, 305 F.2d 859, 134 USPQ 292 (CCPA 1962), a rejection under 35 U.S.C. 103 should not be based on considerable speculation about the meaning of terms employed in a claim or assumptions that must be made as to the scope of the claims”. Therefore, no prior art rejections have been made for claim 23.
Additional References
The following references would also appear to be relevant to Applicant’s invention and are therefore cited in the attached PTO-892:
“An alternative to standard nuclear core control using a multi-objective approach”: discloses a method for controlling a nuclear reactor during a load-follow operation by iteratively measuring a core power and an axial offset, generating an injection sequence of neutron poison and/or water, predicting an evolution of the axial offset using a predictive model, and evaluating a cost function to determine an optimum injection sequence (FIGS. 2, 5, Appendix B, Abstract, pp. 99-100, 102)
“An Automated Boron Management System for WWER-1000 Nuclear Reactors”: discloses a method for controlling a nuclear reactor by determining an optimal injection sequence of neutron poison and/or water and predicting an evolution of a state of the reactor core (FIG. 2, pp. 51-52, 55-56)
“Development of a boron concentration prediction model using multi-cell simulation of the automatic load follow operation”: discloses a method for controlling a nuclear reactor during a load-follow operation by determining an injection sequence of boron and/or water and predicting an evolution of boron concentration in the reactor core using a predictive model (Abstract, pp. 464, 472)
US Patent No. 4,844,856: discloses a method for controlling a nuclear reactor by automatic boration or dilution (Abstract)
US Patent No. 4,647,421: discloses a method for controlling a nuclear reactor during a load-follow operation by generating a boron injection sequence (FIGS. 2, 4, Abstract)
US Publication No. 2025/0149197: discloses a method for controlling a nuclear reactor during a load-follow operation by generating a boron injection sequence based on a calculated evolution of an axial offset of the reactor core (Abstract, claim 20)
The Applied References
For Applicant’s benefit, portions of the applied reference(s) have been cited (as examples) to aid in the review of the rejection(s). While every attempt has been made to be thorough and consistent within the rejection, it is noted that the prior art must be considered in its entirety by Applicant, including any disclosures that may teach away from the claims. See MPEP 2141.02(VI).
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/JINNEY KIL/Examiner, Art Unit 3646
1 https://www.nrc.gov/reading-rm/doc-collections/fact-sheets/new-nuc-plant-des-bg
2 https://www.nrc.gov/reactors/power/pwrs
3 https://www.mathworks.com/help/optim/ug/fmincon.html
4 https://www.mathworks.com/help/optim/ug/choosing-the-algorithm.html
5 https://www.mathworks.com/help/optim/ug/constrained-nonlinear-optimization-algorithms.html#brnpd5f
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