Pavilion Technologies, Inc.

Hsb Solomon Associates, LLC

Other

John P Havener

Stephen William Piche

James David Keeler

Gregory D Martin

Eugene Boe

Mark Gerules

Douglas Timmer

Richard Bradley Jones

Devendra B Godbole

Ralph Bruce Ferguson

William P Horner

Donald Semrad

Russell F Brown

Steven J McGarel

Brian K Stephenson

Ralph Bruce Ferguson, Ii

James David Keller

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 provides analysis of pre- and post-project effects on frequency and severity of operational loss. The method includes the computation of the frequency of incidents and the severity of incidents for equipment based on recorded incident data. Risk, a function of frequency and severity, is calculated before and after a project to determine if the project has changed the risk of an incident in a piece of equipment or facility and if the project has changed the nature of risk (i.e. increasing severity while decreasing frequency). The method may be used to compute forecast estimates of future operational losses, assess goals for improving current performance relative to demonstrated industry performance, and determine statistical confidence intervals of forecast such that risk, and changes in risk, may be visually quantified and communicated.

Graphical risk-based performance measurement and benchmarking system and method

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

A method for controlling a plant to achieve desired operating results. Select operating parameters of the plant are measured and input to a plurality of transforms that define select actions to be taken by an operator of the plant as a function of the measured select operating parameters. Each of the transforms is associated with a portion of the measured select operating parameters and is operable to determine if a predetermined and associated condition exists in the plant, which would warrant the associated action being taken. The measured select operating parameters are processed through the associated transforms to determine for each of the transforms if the associated condition is present. An indication that the condition associated with any of the transforms is present, and for which transform, is provided to a user. A suggestion of the action to be taken for the associated indication is then provided to the user.

Method and apparatus for maximizing power usage in a power plant

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.

An on-line optimizer is provided wherein a boiler () is optimized by measuring a select plurality of inputs to the boiler () and mapping them through a predetermined relationship that defines a single value representing a spacial relationship in the boiler that is a function of the select inputs. This single value is then optimized with the use of a plant optimizer () which provides an optimized value. This optimized value is then processed thought the inverse relationship of the single modified value to provide modified inputs to the plant that can be applied to the plant.

Method for optimizing a plant with multiple inputs

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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Plano, TX, United States of America

John P Havener