Pavilion Technologies, Inc.

Rockwell Automation Technologies Incorporated

Eugene Boe

Stephen William Piche

Gregory D Martin

John P Havener

Mark Gerules

Douglas Timmer

James David Keeler

Steven J McGarel

Keith Alex Smith

Walter Edgar Hayes, Iv

Michael Eugene Sugars

Dennis Tzyy-nian Yieh

William Butler Forbes

Scott Lawrence Jost

Richard Andrew Hovan

James David Keller

Iv Walter Edgar Hayes

One embodiment of the present disclosure describes an industrial system, which includes a control system with a predictive emissions monitoring system that facilitates determining a chemical level output from a selective catalytic reduction unit that reduces the chemical level in gaseous emissions produced by a combustion source using a selective catalytic reduction model. The control system tunes the selective catalytic reduction model by determining tuning parameters based at least in part on vendor information and tuning data determined via a tuning sequence. The tuning sequence includes operating the combustion source at a plurality of load levels, injecting a reactant into received gaseous emissions at each of the plurality of load levels in accordance with an injection rate provided in the vendor information; and determining an input chemical level to and an output chemical level from the selective catalytic reduction unit at each of the plurality of load levels.

Predictive emissions monitor systems and methods

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 continuous emissions model system is described that employs an emissions model that can determine emissions values from a plant for use in place of sensed emission data in the event of failure or lack of communication from a sensor. The emissions model itself receives inputs that may be based upon sensed data. Each emissions model input may be substituted with a modeled input. The modeled inputs, as well as the emissions model outputs maybe biased to render them more accurate. If any one of the emissions model inputs fails (e.g., becomes unavailable or is clearly erroneous), the corresponding modeled input may be utilized so long as the modeled input passes an acceptability test.

Sensor validation and value replacement for continuous emissions monitoring

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

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 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

Dynamic controller for controlling a system

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.

Growing community of inventors

Austin, TX, United States of America

Eugene Boe