Pavilion Technologies, Inc.

Rockwell Automation Technologies Incorporated

Hsb Solomon Associates, LLC

Other

Gregory D Martin

Stephen William Piche

Eugene Boe

John P Havener

Mark Gerules

Douglas Timmer

James David Keeler

Steven J McGarel

Richard Bradley Jones

William P Horner

Russell F Brown

James David Keller

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. 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.

Method and apparatus for minimizing error in dynamic and steady-state processes for prediction, control, and optimization

A system and method is provided for determining the variability induced on a process output. The method includes the analysis of input variable values to determine the total variability. A series of processes may be analyzed and ranked so that a process owner may gain an understanding of how a target process performs relative to the processes of other process owners. The method includes the generation of graphical process comparisons and advice regarding asset performance. The method also includes the estimation of cost impacts due to changes in induced variability.

Control asset comparative performance analysis system and methodology

A system and method for predicting operation of a plant or process receive an input value from the plant or process. An integrity of a non-linear model corresponding to a local input space of the input value may be determined. The non-linear model may include an empirical representation of the plant or process. If the integrity is above a first threshold, non-linear model may be used to provide a first output value. However, if the integrity is below the first threshold, a linearized first principles model may be used to provide a second output value. The linearized first principles model may include an analytic representation of the plant or process. Additionally, the analytic representation of the plant or process may be independent of the empirical representation of the plant or process. The first output value and/or the second output value may be usable to manage the plant or process.

System and method for utilizing a hybrid model

Dynamic controller utilizing a hybrid model

System and method for parameterizing one or more steady-state models each having a plurality of model parameters for mapping model input to model output through a stored representation of an in-situ hydrocarbon reservoir. For each model, training data representing operation of the reservoir is provided including input values and target output values. A next input value(s) and next target output value are received from the training data. The model is parameterized with the input value(s) and target output value, and derivative constraints imposed to constrain relationships between the input value(s) and a resulting model output value, using an optimizer to perform constrained optimization on the parameters to satisfy an objective function subject to the derivative constraints. The receiving and parameterizing are performed iteratively, generating a parameterized model. Multiple models form an aggregate model of the system/process, which may be optimized to satisfy a second objective function subject to operational constraints.

Modeling in-situ reservoirs with derivative 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.

Method and apparatus for attenuating error in 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 approximating gains in dynamic and steady-state processes for prediction, control, and optimization

A method for controlling a non-linear process includes the steps of first providing a controller that is operable to receive inputs representing measured variables of the process and predicting on an output of the controller predicted control values for manipulatible variables that control the process. An expert system is provided that models the actions of an operator of the process over an operating region of the process that represents a set of rules for actions to be taken by an operator upon the occurrence of predetermined conditions in the operation of the process. The operation of the controller is modified with the expert system when one of the predetermined conditions exists.

Kiln control and upset recovery using a model predictive control in series with forward chaining

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.

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Georgetown, TX, United States of America

Gregory D Martin