Pavilion Technologies, Inc.

Rockwell Automation Technologies Incorporated

Mark Gerules

Stephen William Piche

James David Keeler

Gregory D Martin

John P Havener

Eugene Boe

Douglas Timmer

Eric J Hartman

Steven J McGarel

Kadir Liano

William Douglas Johnson

James David Keller

Method and apparatus for training a system model with gain constraints. A method is disclosed for training a steady-state model, the model having an input and an output and a mapping layer for mapping the input to the output through a stored representation of a system. A training data set is provided having a set of input data u(t) and target output data y(t) representative of the operation of a system. The model is trained with a predetermined training algorithm which is constrained to maintain the sensitivity of the output with respect to the input substantially within user defined constraint bounds by iteratively minimizing an objective function as a function of a data objective and a constraint objective. The data objective has a data fitting learning rate and the constraint objective has constraint learning rate that are varied as a function of the values of the data objective and the constraint objective after selective iterative steps.

Method and apparatus for training a system model with gain constraints using a non-linear programming optimizer

Method and apparatus for optimizing a system model with gain constraints using a non-linear programming optimizer

Method and apparatus for training a system model with gain constraints. A method is disclosed for training a steady-state model having an input and an output and a mapping layer for mapping the input to the output, the model comprising a stored representation of a plant or process, and including a linear portion and a non-linear portion, where the non-linear portion includes a function. Input is received to the model, and predicted output computed corresponding to attribute(s) of the plant or process. The predicted output is stored, and is usable to manage the plant or process. The model is trained to optimize a specified objective function subject to one or more constraints, e.g., via a non-linear programming (NLP) optimizer, the constraints including, hard constraint(s) comprising strict limitations on the training in optimizing the objective function, and/or soft constraint(s) comprising a weighted penalty function included in the objective function.

Method and apparatus for training a system model including an integrated sigmoid function

A kiln thermal and combustion control. A predictive model is provided of the dynamics of selected aspects of the operation of the system for modeling the dynamics thereof. The model has at least two discrete models associated therewith that model at least two of the selected aspects, the at least two discrete models having different dynamic responses. An optimizer receives desired values for the selected aspects of the operation of the system modeled by the model and optimizes the inputs to the model to minimize error between the predicted and desired values. A control input device then applies the optimized input values to the system after optimization thereof.

Kiln free lime control

Kiln thermal and combustion control

Method and apparatus for training a system model with gain constraints

A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model () and an independent dynamic model (). The static model () is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model () is trained over a narrow range of data. The gain K of the static model () is utilized to scale the gain k of the dynamic model (). The forced dynamic portion of the model () referred to as the bvariables are scaled by the ratio of the gains K and k. The bhave a direct effect on the gain of a dynamic model (). This is facilitated by a coefficient modification block (). Thereafter, the difference between the new value input to the static model () and the prior steady-state value is utilized as an input to the dynamic model (). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y. Additionally, the path that is traversed between steady-state value changes.

Dynamic controller for controlling a system

A method and apparatus for controlling a non-linear mill. A linear controller is provided having a linear gain k that is operable to receive inputs representing measured variables of the plant and predict on an output of the linear controller predicted control values for manipulatible variables that control the plant. A non-linear model of the plant is provided for storing a representation of the plant over a trained region of the operating input space and having a steady-state gain K associated therewith. The gain k of the linear model is adjusted with the gain K of the non-linear model in accordance with a predetermined relationship as the measured variables change the operating region of the input space at which the linear controller is predicting the values for the manipulatible variables. The predicted manipulatible variables are then output after the step of adjusting the gain k.

A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model ( ) and an independent dynamic model ( ). The static model ( ) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model ( ) is trained over a narrow range of data. The gain K of the static model ( ) is utilized to scale the gain k of the dynamic model ( ). The forced dynamic portion of the model ( ) referred to as the b variables are scaled by the ratio of the gains K and k. The b have a direct effect on the gain of a dynamic model ( ). This is facilitated by a coefficient modification block ( ). Thereafter, the difference between the new value input to the static model ( ) and the prior steady-state value is utilized as an input to the dynamic model ( ). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y. Additionally, the path that is traversed between steady-state value changes.

Method and apparatus for modeling dynamic and steady-state processes for prediction, control and optimization

A method and apparatus for controlling a non-linear mill. A linear controller is provided having a linear gain k that is operable to receive inputs representing measured variables of the plant and predict on an output of the linear controller predicted control values for manipulatible variables that control the plant. A non-linear model of the plant is provided for storing a representation of the plant over a trained region of the operating input space and having a steady-state gain K associated therewith. The gain k of the linear model is adjusted with the gain K of the non-linear model in accordance with a predetermined relationship as the measured variables change the operating region of the input space at which the linear controller is predicting the values for the manipulatible variables. The predicted manipulatible variables are then output after the step of adjusting the gain k

Method and apparatus for controlling a non-linear mill

A kiln thermal and combustion control. A predictive model is provided of the dynamics of selected aspects of the operation of the plant for modeling the dynamics thereof. The model has at least two discrete models associated therewith that model at least two of the selected aspects, the at least two discrete models having different dynamic responses. An optimizer receives desired values for the selected aspects of the operation of the plant modeled by the model and optimizes the inputs to the model to minimize error between the predicted and desired values. A control input device then applies the optimized input values to the plant after optimization thereof.

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Cedar Park, TX, United States of America

Mark Gerules