Pavilion Technologies, Inc.

Rockwell Automation Technologies Incorporated

Steven J McGarel

Gregory D Martin

Stephen William Piche

James David Keeler

John P Havener

Eugene Boe

Mark Gerules

Douglas Timmer

Manoel Augusto Morales Do Nascimento

A technique is disclosed for controlling a material blending process for blending a plurality of raw material feeds using model predictive control in order to produce a blended product conforming to one or more quality standards. The quality standards may be based upon control variables derived from relationships and/or ratios of certain materials in the composition of the raw material feeds (e.g., moduli). The determined control variables are compared to desired set points and, if a deviation from the desired set points is detected, the proportions of the raw material feeds are adjusted to align the control variables with the desired set points. The technique may further take material cost factors into account, wherein the prediction model determines a proper proportioning of the raw material feeds such that the blended product conforms to the desired specifications and is produced for the lowest cost.

Bulk material blending control

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.

Kiln free lime control

Kiln thermal and combustion control

Kiln/cooler control and upset recovery using a combination of model predictive control and expert systems. 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 overridden with the expert system when one of the predetermined conditions exists and taking the associated action by the expert system to control the operation of the process by changing one or more of the manipulatible variables.

Kiln/cooler control and upset recovery using a combination of model predictive control and expert systems

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Austin, TX, United States of America

Steven J McGarel