Patents by Inventor Eugene Boe
Eugene Boe has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20060229743Abstract: A method for providing independent static and dynamic models in a 000 prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the bi variables are scaled by the ratio of the gains K and k. The bi have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y.Type: ApplicationFiled: February 21, 2006Publication date: October 12, 2006Inventors: Eugene Boe, Stephen Piche, Gregory Martin
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Patent number: 7110834Abstract: 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.Type: GrantFiled: September 23, 2003Date of Patent: September 19, 2006Assignee: Pavilion Technologies, Inc.Inventors: Gregory D. Martin, Eugene Boe, Stephen Piche, James David Keeler, Douglas Timmer, Mark Gerules, John P. Havener, Steven J. McGarel
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Patent number: 7050866Abstract: A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the bi variables are scaled by the ratio of the gains K and k. The bi have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y.Type: GrantFiled: May 17, 2004Date of Patent: May 23, 2006Assignee: Pavilion Technologies, Inc.Inventors: Gregory D. Martin, Eugene Boe, Stephen Piche, James David Keeler, Douglas Timmer, Mark Gerules, John P. Havener
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Patent number: 7047089Abstract: 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.Type: GrantFiled: May 11, 2004Date of Patent: May 16, 2006Assignee: Pavilion TechnologiesInventors: Gregory D. Martin, Eugene Boe, Stephen Piche, James David Keeler, Douglas Timmer, Mark Gerules, John P. Havener
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Publication number: 20060100720Abstract: 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.Type: ApplicationFiled: December 22, 2005Publication date: May 11, 2006Inventors: Gregory Martin, Eugene Boe, Stephen Piche, James Keeler, Douglas Timmer, Mark Gerules, John Havener, Steven McGarel
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Patent number: 7024252Abstract: 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.Type: GrantFiled: September 26, 2005Date of Patent: April 4, 2006Assignee: Pavilion Technologies, Inc.Inventors: Gregory D. Martin, Eugene Boe, Stephen Piche, James David Keeler, Douglas Timmer, Mark Gerules, John P. Havener
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Publication number: 20060020352Abstract: 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.Type: ApplicationFiled: September 26, 2005Publication date: January 26, 2006Inventors: Gregory Martin, Eugene Boe, Stephen Piche, James Keeler, Douglas Timmer, Mark Gerules, John Havener
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Publication number: 20050075737Abstract: A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the bi variables are scaled by the ratio of the gains K and k. The bi have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y.Type: ApplicationFiled: May 17, 2004Publication date: April 7, 2005Inventors: Gregory Martin, Eugene Boe, Stephen Piche, James Keeler, Douglas Timmer, Mark Gerules, John Havener
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Publication number: 20040210325Abstract: 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.Type: ApplicationFiled: May 11, 2004Publication date: October 21, 2004Inventors: Gregory D. Martin, Eugene Boe, Stephen Piche, James David Keeler, Douglas Timmer, Mark Gerules, John P. Havener
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Patent number: 6738677Abstract: A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the bi variables are scaled by the ratio of the gains K and k. The bi have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y.Type: GrantFiled: November 22, 2002Date of Patent: May 18, 2004Assignee: Pavilion Technologies, Inc.Inventors: Gregory D. Martin, Eugene Boe, Stephen Piche, James David Keeler, Douglas Timmer, Mark Gerules, John P. Havener
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Patent number: 6735483Abstract: 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.Type: GrantFiled: December 9, 2002Date of Patent: May 11, 2004Assignee: Pavilion Technologies, Inc.Inventors: Gregory D. Martin, Eugene Boe, Stephen Piche, James David Keeler, Douglas Timmer, Mark Gerules, John P. Havener
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Publication number: 20040059441Abstract: 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.Type: ApplicationFiled: September 23, 2003Publication date: March 25, 2004Inventors: Gregory D. Martin, Eugene Boe, Stephen Piche, James David Keeler, Douglas Timmer, Mark Gerules, John P. Havener
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Patent number: 6625501Abstract: 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.Type: GrantFiled: August 14, 2002Date of Patent: September 23, 2003Assignee: Pavilion Technologies, Inc.Inventors: Gregory D. Martin, Eugene Boe, Stephen Piche, James David Keeler, Douglas Timmer, Mark Gerules, John P. Havener
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Publication number: 20030088322Abstract: 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.Type: ApplicationFiled: August 14, 2002Publication date: May 8, 2003Inventors: Gregory D. Martin, Eugene Boe, Stephen Piche, James David Keeler, Douglas Timmer, Mark Gerules, John P. Havener
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Publication number: 20030078684Abstract: A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the bl variables are scaled by the ratio of the gains K and k. The bi have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y.Type: ApplicationFiled: November 22, 2002Publication date: April 24, 2003Inventors: Gregory D. Martin, Eugene Boe, Stephen Piche, James David Keeler, Douglas Timmer, Mark Gerules, John P. Havener
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Publication number: 20030065410Abstract: 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.Type: ApplicationFiled: December 9, 2002Publication date: April 3, 2003Inventors: Gregory D. Martin, Eugene Boe, Stephen Piche, James David Keeler, Douglas Timmer, Mark Gerules, John P. Havener
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Patent number: 6493596Abstract: 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.Type: GrantFiled: February 28, 2000Date of Patent: December 10, 2002Assignee: Pavilion Technologies, Inc.Inventors: Gregory D. Martin, Eugene Boe, Stephen Piche, James David Keller, Douglas Timmer, Mark Gerules, John P. Havener
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Patent number: 6487459Abstract: A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the bi variables are scaled by the ratio of the gains K and k. The bi have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y.Type: GrantFiled: February 16, 1999Date of Patent: November 26, 2002Assignee: Pavilion Technologies, Inc.Inventors: Gregory D. Martin, Eugene Boe, Stephen Piche, James David Keeler, Douglas Timmer, Mark Gerules, John P. Havener
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Patent number: 6438430Abstract: 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.Type: GrantFiled: May 9, 2000Date of Patent: August 20, 2002Assignee: Pavilion Technologies, Inc.Inventors: Gregory D. Martin, Eugene Boe, Stephen Piche, James David Keeler, Douglas Timmer, Mark Gerules, John P. Havener
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Patent number: D451891Type: GrantFiled: August 22, 2000Date of Patent: December 11, 2001Assignee: Deere & CompanyInventors: James Thomas Noonan, Shane Julius Brueggen, Paul David Parker, Thomas Eugene Boe, Alan Alfred Larson, Bruce Craig Newendorp, Nancy Esther Post, James Edward Sabelka, Richard James Livdahl