Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.

Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.

Abstract: System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g.

Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.

Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.

Abstract: The present invention provides novel techniques for optimizing and controlling production plants using parametric multifaceted models. In particular, the parametric multifaceted models may be configured to convert a first set of parameters (e.g., control parameters) relating to a production plant into a second set of parameters (e.g., optimization parameters) relating to the production plant. In general, the first set of parameters will be different than the second set of parameters. For example, the first set of parameters may be indicative of low-level, real-time control parameters and the second set of parameters may be indicative of high-level, economic parameters. Utilizing appropriate parameterization may allow the parametric multifaceted models to deliver an appropriate level of detail of the production plant within a reasonable amount of time.

Abstract: A technique is disclosed for reducing an error in a controlled variable via model predictive control. A predicted error in the controlled variable is determined for a forward-looking control horizon based upon measured or computed variables. The integral of the predicted error is computed. If the error or the integral exceed a tolerance for a determined time period, the model predictive control algorithm is modified to drive the error or the integral to within a tolerance. The modifications to the control algorithm may include changes to coefficients for terms based upon the error and/or the integral of the error.

Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.

Abstract: The present invention provides novel techniques for optimizing and controlling production plants using parametric multifaceted models. In particular, the parametric multifaceted models may be configured to convert a first set of parameters (e.g., control parameters) relating to a production plant into a second set of parameters (e.g., optimization parameters) relating to the production plant. In general, the first set of parameters will be different than the second set of parameters. For example, the first set of parameters may be indicative of low-level, real-time control parameters and the second set of parameters may be indicative of high-level, economic parameters. Utilizing appropriate parameterization may allow the parametric multifaceted models to deliver an appropriate level of detail of the production plant within a reasonable amount of time.

Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.

Abstract: The present invention provides novel techniques for optimizing and controlling production plants using parametric multifaceted models. In particular, the parametric multifaceted models may be configured to convert a first set of parameters (e.g., control parameters) relating to a production plant into a second set of parameters (e.g., optimization parameters) relating to the production plant. In general, the first set of parameters will be different than the second set of parameters. For example, the first set of parameters may be indicative of low-level, real-time control parameters and the second set of parameters may be indicative of high-level, economic parameters. Utilizing appropriate parameterization may allow the parametric multifaceted models to deliver an appropriate level of detail of the production plant within a reasonable amount of time.

Abstract: System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g.

Abstract: The present invention provides novel techniques for controlling batch reaction processes. In particular, a parametric hybrid model may be used to parameterize inputs and outputs of batch reaction processes. The parametric hybrid model may include an empirical model, a parameter model, and a dynamic model. Critical quality parameters, which are correlated with, but not the same as, end-of-batch quality values for the batch reaction processes may be monitored during cycles of the batch reaction processes. The quality parameters may be used to generate desired batch trajectories, which may be used to control the batch reaction processes during the cycles of the batch reaction processes.

Abstract: The present invention provides novel techniques for graphically modeling, displaying, and interacting with parametric hybrid models used to optimize and control components of industrial plants and enterprises. In particular, a graphical modeling tool of a control/optimization system for controlling a plant or enterprise is configured to transmit a graphical user interface to a user, wherein the graphical user interface enables a plurality of command inputs relating to a plurality of parametric hybrid models based on a security access level of the user. The parametric hybrid models may be displayed by the graphical user interface as nodes of a network with connections connecting the nodes. The user may graphically manipulate the nodes and connections associated with the parametric hybrids models to either modify optimization constraints of the model network, or actually modify the manner in which the parametric hybrid models function (e.g.

Abstract: The present invention provides novel techniques for graphically modeling, displaying, and interacting with parametric hybrid models used to optimize and control components of industrial plants and enterprises. In particular, a graphical modeling tool of a control/optimization system for controlling a plant or enterprise is configured to transmit a graphical user interface to a user, wherein the graphical user interface enables a plurality of command inputs relating to a plurality of parametric hybrid models based on a security access level of the user. The parametric hybrid models may be displayed by the graphical user interface as nodes of a network with connections connecting the nodes. The user may graphically manipulate the nodes and connections associated with the parametric hybrids models to either modify optimization constraints of the model network, or actually modify the manner in which the parametric hybrid models function (e.g.

Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.

Abstract: The present invention provides novel techniques for controlling energy systems. In particular, parametric hybrid models may be used to parameterize inputs and outputs of groups of equipment of energy systems. Each parametric hybrid model may include an empirical model, a parameter model, and a dynamic model. Critical parameters for groups of equipment modeled by the parametric hybrid models, which are correlated with, but not the same as, input and output variables of the groups of equipment may be monitored during operation of the energy system. The critical parameters may be used to generate optimal trajectories for the energy system, which may be used to control the energy system.

Abstract: The present invention provides novel techniques for graphically modeling, displaying, and interacting with parametric hybrid models used to optimize and control components of industrial plants and enterprises. In particular, a graphical modeling tool of a control/optimization system for controlling a plant or enterprise is configured to transmit a graphical user interface to a user, wherein the graphical user interface enables a plurality of command inputs relating to a plurality of parametric hybrid models based on a security access level of the user. The parametric hybrid models may be displayed by the graphical user interface as nodes of a network with connections connecting the nodes. The user may graphically manipulate the nodes and connections associated with the parametric hybrids models to either modify optimization constraints of the model network, or actually modify the manner in which the parametric hybrid models function (e.g.

Abstract: The present invention provides novel techniques for graphically modeling, displaying, and interacting with parametric hybrid models used to optimize and control components of industrial plants and enterprises. In particular, a graphical modeling tool of a control/optimization system for controlling a plant or enterprise is configured to transmit a graphical user interface to a user, wherein the graphical user interface enables a plurality of command inputs relating to a plurality of parametric hybrid models based on a security access level of the user. The parametric hybrid models may be displayed by the graphical user interface as nodes of a network with connections connecting the nodes. The user may graphically manipulate the nodes and connections associated with the parametric hybrids models to either modify optimization constraints of the model network, or actually modify the manner in which the parametric hybrid models function (e.g.

Abstract: The present invention provides novel techniques for graphically modeling, displaying, and interacting with parametric hybrid models used to optimize and control components of industrial plants and enterprises. In particular, a graphical modeling tool of a control/optimization system for controlling a plant or enterprise is configured to transmit a graphical user interface to a user, wherein the graphical user interface enables a plurality of command inputs relating to a plurality of parametric hybrid models based on a security access level of the user. The parametric hybrid models may be displayed by the graphical user interface as nodes of a network with connections connecting the nodes. The user may graphically manipulate the nodes and connections associated with the parametric hybrids models to either modify optimization constraints of the model network, or actually modify the manner in which the parametric hybrid models function (e.g.