Granted Patent B2

US 12,523,970 · App. 17/810,169 · Granted Jan 13, 2026

Inventors: Hong Zhao (Sugar Land, TX); Christopher Quan (Somerville, MA); Shu Wang (Lexington, MA); Melisa Morales Cárdenas (Mexico City, MX)

Assignee: AspenTech Corporation

G05B13/048G05B13/0265

Abstract

Claims (104)

1 . A computer-implemented method of creating a model for operating a real-world industrial process, the method comprising:

training a reduced order model (ROM) of a subject industrial process using simulation data generated from one or more simulations of the subject industrial process performed using a first-principles model (FPM) of the subject industrial process;

constructing a sustainable dynamic reduced-order model (SDROM) of the subject industrial process by:

(i) dividing the trained ROM into a plurality of multiple-input single output (MISO) sub-models;

(ii) partitioning each MISO sub-model into multiple component terms; and

(iii) inserting a gain factor into each partitioned component term to form a gain vector for each MISO sub-model; and

deploying the SDROM online to operate the subject industrial process, wherein deploying the SDROM online includes:

receiving a stream of real-time data of the subject industrial process; and

determining, using the received stream of real-time data, optimal operation settings of the subject industrial process based on a predicted output and estimated local gains from the plurality of MISO sub-models.

2 . The method of claim 1 wherein deploying the SDROM online includes:

obtaining operation data of the subject industrial process; and

periodically calibrating and validating the deployed SDROM using the obtained operation data.

3 . The method of claim 2 wherein periodically calibrating and validating the deployed SDROM includes:

monitoring a given MISO sub-model of the plurality of MISO sub-models by:

(i) retrieving historical operation data from a plant history database;

(ii) filtering elements of the retrieved historical operation data with a dynamic filter attached to a given input of the given MISO sub-model;

(iii) wherein the gain vector formed for the given MISO sub-model is a first gain vector, estimating gain factors of a second gain vector of the given MISO sub-model based on the filtered elements of the retrieved historical operation data;

(iv) comparing the estimated gain factors of the second gain vector with inserted gain factors of the first gain vector to determine model performance metrics; and

(v) displaying, to users, a visual representation of the determined model performance metrics.

4 . The method of claim 2 wherein periodically calibrating and validating the constructed SDROM includes:

adapting a given MISO sub-model of the plurality of MISO sub-models of the constructed SDROM with historical operation data of the subject industrial process by:

receiving allowance to perform adaptations to at least a part of the given MISO sub-model of the constructed SDROM from a user or an auto-system; and

updating the given MISO sub-model by calibrating gain factors of the given MISO sub-model.

5 . The method of claim 4 wherein:

updating the given MISO sub-model by calibrating gain factors of the given MISO sub-model includes:

retrieving the historical operation data, as a time-series, from a plant history database, for a window of time represented within the plant history database;

filtering elements of the retrieved historical operation data with a dynamic filter attached to a given input of the given MISO sub-model;

feeding the filtered elements of the retrieved historical operation data as inputs into the given MISO sub-model and predicting an output time-series corresponding to the filtered elements;

comparing the predicted output with the retrieved historical operation data of the subject industrial process output in R 2 statistics;

adjusting, according to the comparing, and subject to one or more suppression constraints, values of the gain factors for the given input of the given MISO sub-model, thereby calibrating the gain factors of the given MISO sub-model;

updating the SDROM to include the given MISO sub-model with the calibrated gain factors; and

validating the updated SDROM with a testing dataset.

6 . The method of claim 1 wherein deploying the SDROM online further includes:

filtering elements of the received stream of real-time data with a dynamic filter attached to a given input of a given MISO sub-model; and

predicting an output of the given MISO sub-model and estimating local gains of the given MISO sub-model around an updated operating point determined from the filtered elements of the received stream of real-time data.

7 . The method of claim 1 wherein deploying the SDROM online includes:

determining the optimal operation settings of the subject industrial process using the SDROM and an optimization objective function, the optimal operation settings including at least one of a temperature setpoint, a pressure setpoint, a flow rate setpoint, an operating throughput, and a transition path from a current operating state to the determined optimal operation settings; and

operating the subject industrial process in accordance with the determined optimal operation settings.

8 . The method of claim 1 wherein constructing the SDROM further includes:

attaching respective dynamic filters to inputs of each MISO sub-model to align, with respect to time, input data.

9 . The method of claim 8 wherein attaching respective dynamic filters to inputs of each MISO sub-model includes at least one of:

obtaining at least one of the respective dynamic filters from a dynamic finite impulse response (FIR) model by use of a linear model order reduction technique; and

obtaining at least one of the respective dynamic filters from input-output time-series data of the subject industrial process by use of a subspace identification technique.

10 . The method of claim 1 wherein training the ROM of the subject industrial process includes:

dividing the generated simulation data into a training dataset and a testing dataset;

establishing the ROM by fitting the training dataset to a polynomial-based implementation of the FPM with polynomials of a 2 nd , or higher, order; and

validating the established ROM with the testing dataset.

11 . The method of claim 1 wherein training the ROM further includes at least one of:

configuring the FPM of the subject industrial process based on at least one of: (i) a flowsheet of the subject industrial process, (ii) a design of the subject industrial process, and (iii) operational parameters of the subject industrial process; and

generating the generated simulation data by performing the one or more simulations with variable values encompassing input variations and historical operation of the subject industrial process.

12 . The method of claim 11 wherein configuring the FPM of the subject industrial process includes at least one of:

building one or more simulation blocks and connections, according to the flowsheet of the subject industrial process; and

configuring one or more designs of the subject industrial process, or operational parameters of the subject industrial process, in a given simulation of the one or more simulations.

13 . The method of claim 11 wherein generating the generated simulation data by performing the one or more simulations includes:

running multiple simulations of the subject industrial process based on a plurality of combinations of input values and operating conditions; and

recording simulation output data from the multiple simulations as the generated simulation data.

14 . A computer-based system for creating a model for operating a real-world industrial process, the system comprising:

a processor; and

a memory with computer code instructions stored thereon, the processor and the memory, with the computer code instructions, being configured to cause the system to:

train a reduced order model (ROM) of a subject industrial process using simulation data generated from one or more simulations of the subject industrial process performed using a first-principles model (FPM) of the subject industrial process;

construct a sustainable dynamic reduced-order model (SDROM) of the subject industrial process by:

(i) dividing the trained ROM into a plurality of multiple-input single-output (MISO) sub-models;

(ii) partitioning each MISO sub-model into multiple component terms; and

(iii) inserting a gain factor into each partitioned component term to form a gain vector for each MISO sub-model; and

deploy the SDROM online to operate the subject industrial process,

wherein deploying the SDROM online includes:

receiving a stream of real-time data of the subject industrial process; and

determining, using the received stream of real-time data, optimal operation settings of the subject industrial process based on a predicted output and estimated local gains from the plurality of MISO sub-models.

15 . The system of claim 14 wherein, in deploying the SDROM online, the processor and the memory, with the computer code instructions, are further configured to cause the system to:

obtain operation data of the subject industrial process; and

monitor a given MISO sub-model of the plurality of MISO sub-models by:

(i) retrieving historical operation data from a plant history database;

(ii) filtering elements of the retrieved historical operation data with a dynamic filter attached to a given input of the given MISO sub-model;

(iii) wherein the gain vector formed for the given MISO sub-model is a first gain vector, estimating gain factors of a second gain vector of the given MISO sub-model based on the filtered elements of the retrieved historical operation data;

(iv) comparing the estimated gain factors of the second gain vector with inserted gain factors of the first gain vector to determine model performance metrics; and

(v) displaying, to users, a visual representation of the determined model performance metrics.

16 . The system of claim 14 wherein, in deploying the SDROM online, the processor and the memory, with the computer code instructions, are further configured to cause the system to:

adapt a given MISO sub-model of the plurality of MISO sub-models of the constructed SDROM with historical operation data of the subject industrial process by:

receiving allowance to perform adaptations to at least a part of the given MISO sub-model of the constructed SDROM from a user or an auto-system; and

updating the given MISO sub-model by calibrating gain factors of the given MISO sub-model.

17 . The system of claim 16 wherein, in updating the given MISO sub-model by calibrating gain factors of the given MISO sub-model, the processor and the memory, with the computer code instructions, are further configured to cause the system to:

retrieve the historical operation data, as a time-series, from a plant history database, for a window of time represented within the plant history database;

filter elements of the retrieved historical operation data with a dynamic filter attached to a given input of the given MISO sub-model;

feed the filtered elements of the retrieved historical operation data as inputs into the given MISO sub-model and predict an output time-series corresponding to the filtered elements;

compare the predicted output with the retrieved historical operation data of the subject industrial process in R 2 statistics;

adjust, according to the comparing, and subject to one or more suppression constraints, values of the gain factors for the given input of the given MISO sub-model, thereby calibrating the gain factors of the given MISO sub-model;

update the SDROM to include the given MISO sub-model with the calibrated gain factors; and

validate the updated SDROM with the testing dataset.

18 . The system of claim 14 wherein, in deploying the SDROM online, the processor and the memory, with the computer code instructions, are further configured to cause the system to:

filter elements of the received stream of real-time data with a dynamic filter attached to a given input of a given MISO sub-model; and

predict an output of the given MISO sub-model and estimate local gains of the given MISO sub-model around an updated operating point determined from the filtered elements of the received stream of real-time data.

19 . The system of claim 14 wherein, in deploying the SDROM online, the processor and the memory, with the computer code instructions, are further configured to cause the system to:

determine the optimal operation settings of the subject industrial process using the SDROM and an optimization objective function, the optimal operation settings including at least one of a temperature setpoint, a pressure setpoint, a flow rate setpoint, an operating throughput, and a transition path from a current operating state to the determined optimal operation settings; and

operate the subject industrial process in accordance with the determined optimal operation settings.

20 . A computer program product for creating a model for operating a real-world industrial process, the computer program product comprising:

a non-transitory computer-readable medium having computer-readable program instructions stored thereon, the instructions, when executed by a processor, causing the processor to:

train a reduced order model (ROM) of a subject industrial process using simulation data generated from one or more simulations of the subject industrial process performed using a first-principles model (FPM) of the subject industrial process;

construct a sustainable dynamic reduced-order model (SDROM) of the subject industrial process by:

(i) dividing the trained ROM into a plurality of multiple-input single-output (MISO) sub-models;

(ii) partitioning each MISO sub-model into multiple component terms; and

(iii) inserting a gain factor into each partitioned component term to form a gain vector for each MISO sub-model; and

deploy the SDROM online to operate the subject industrial process, wherein deploying the SDROM online includes:

receiving a stream of real-time data of the subject industrial process; and

determining, using the received stream of real-time data, optimal operation settings of the subject industrial process based on a predicted output and estimated local gains determining optimal operation settings of the subject industrial process based on a predicted output and estimated local gains from the plurality of MISO sub-models.

Assignments (3)

Continuity (1)

References Cited (11)