Patents by Inventor Carl Anthony Schweiger
Carl Anthony Schweiger 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: 20230273575Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.Type: ApplicationFiled: May 5, 2023Publication date: August 31, 2023Inventors: Bijan Sayyar-Rodsari, Eric Jon Hartman, Carl Anthony Schweiger
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Patent number: 11675319Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.Type: GrantFiled: July 21, 2020Date of Patent: June 13, 2023Assignee: Rockwell Automation Technology, Inc.Inventors: Bijan Sayyar-Rodsari, Eric Jon Hartman, Carl Anthony Schweiger
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Publication number: 20200348630Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.Type: ApplicationFiled: July 21, 2020Publication date: November 5, 2020Inventors: Bijan Sayyar-Rodsari, Eric Jon Hartman, Carl Anthony Schweiger
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Patent number: 10739735Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.Type: GrantFiled: September 28, 2015Date of Patent: August 11, 2020Assignee: Rockwell Automation Technologies, Inc.Inventors: Bijan Sayyar-Rodsari, Eric Jon Hartman, Carl Anthony Schweiger
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Patent number: 10067485Abstract: The present invention provides novel techniques for optimizing and controlling production plants using parametric multifaceted models. In particular, the parametric multifaceted models may be configured to convert a first set of parameters (e.g., control parameters) relating to a production plant into a second set of parameters (e.g., optimization parameters) relating to the production plant. In general, the first set of parameters will be different than the second set of parameters. For example, the first set of parameters may be indicative of low-level, real-time control parameters and the second set of parameters may be indicative of high-level, economic parameters. Utilizing appropriate parameterization may allow the parametric multifaceted models to deliver an appropriate level of detail of the production plant within a reasonable amount of time.Type: GrantFiled: September 21, 2015Date of Patent: September 4, 2018Assignee: Rockwell Automation Technologies, Inc.Inventors: Bijan Sayyar-Rodsari, Carl Anthony Schweiger
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Publication number: 20160018795Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.Type: ApplicationFiled: September 28, 2015Publication date: January 21, 2016Inventors: Bijan Sayyar-Rodsari, Eric Jon Hartman, Carl Anthony Schweiger
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Publication number: 20160011572Abstract: The present invention provides novel techniques for optimizing and controlling production plants using parametric multifaceted models. In particular, the parametric multifaceted models may be configured to convert a first set of parameters (e.g., control parameters) relating to a production plant into a second set of parameters (e.g., optimization parameters) relating to the production plant. In general, the first set of parameters will be different than the second set of parameters. For example, the first set of parameters may be indicative of low-level, real-time control parameters and the second set of parameters may be indicative of high-level, economic parameters. Utilizing appropriate parameterization may allow the parametric multifaceted models to deliver an appropriate level of detail of the production plant within a reasonable amount of time.Type: ApplicationFiled: September 21, 2015Publication date: January 14, 2016Inventors: Bijan Sayyar-Rodsari, Carl Anthony Schweiger
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Patent number: 9147153Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.Type: GrantFiled: November 6, 2012Date of Patent: September 29, 2015Assignee: ROCKWELL AUTOMATION TECHNOLOGIES, INC.Inventors: Bijan Sayyar-Rodsari, Eric Jon Hartman, Carl Anthony Schweiger
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Patent number: 9141098Abstract: The present invention provides novel techniques for optimizing and controlling production plants using parametric multifaceted models. In particular, the parametric multifaceted models may be configured to convert a first set of parameters (e.g., control parameters) relating to a production plant into a second set of parameters (e.g., optimization parameters) relating to the production plant. In general, the first set of parameters will be different than the second set of parameters. For example, the first set of parameters may be indicative of low-level, real-time control parameters and the second set of parameters may be indicative of high-level, economic parameters. Utilizing appropriate parameterization may allow the parametric multifaceted models to deliver an appropriate level of detail of the production plant within a reasonable amount of time.Type: GrantFiled: October 30, 2009Date of Patent: September 22, 2015Assignee: Rockwell Automation Technologies, Inc.Inventors: Bijan Sayyar-Rodsari, Carl Anthony Schweiger
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Publication number: 20140129491Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.Type: ApplicationFiled: November 6, 2012Publication date: May 8, 2014Applicant: ROCKWELL AUTOMATION TECHNOLOGIES, INC.Inventors: Bijan Sayyar-Rodsari, Eric Jon Hartman, Carl Anthony Schweiger
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Patent number: 8452719Abstract: The present disclosure provides novel techniques for defining empirical models having control, prediction, and optimization modalities. The empirical models may include neural networks and support vector machines. The empirical models may include asymptotic analysis as part of the model definition as allow the models to achieve enhanced results, including enhanced high-order behaviors. The high-order behaviors may exhibit gains that are non-zero trending, which may be useful for controller modalities.Type: GrantFiled: June 29, 2010Date of Patent: May 28, 2013Assignee: Rockwell Automation Technologies, Inc.Inventors: Kadir Liano, Bijan Sayyarrodsari, Carl Anthony Schweiger
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Publication number: 20110320386Abstract: The present disclosure provides novel techniques for defining empirical models having control, prediction, and optimization modalities. The empirical models may include neural networks and support vector machines. The empirical models may include asymptotic analysis as part of the model definition as allow the models to achieve enhanced results, including enhanced high-order behaviors. The high-order behaviors may exhibit gains that are non-zero trending, which may be useful for controller modalities.Type: ApplicationFiled: June 29, 2010Publication date: December 29, 2011Applicant: Rockwell Automation Technologies, Inc.Inventors: Kadir Liano, Bijan Sayyarrodsari, Carl Anthony Schweiger
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Publication number: 20110106277Abstract: The present invention provides novel techniques for optimizing and controlling production plants using parametric multifaceted models. In particular, the parametric multifaceted models may be configured to convert a first set of parameters (e.g., control parameters) relating to a production plant into a second set of parameters (e.g., optimization parameters) relating to the production plant. In general, the first set of parameters will be different than the second set of parameters. For example, the first set of parameters may be indicative of low-level, real-time control parameters and the second set of parameters may be indicative of high-level, economic parameters. Utilizing appropriate parameterization may allow the parametric multifaceted models to deliver an appropriate level of detail of the production plant within a reasonable amount of time.Type: ApplicationFiled: October 30, 2009Publication date: May 5, 2011Applicant: Rockwell Automation Technologies, Inc.Inventors: Bijan Sayyar-Rodsari, Carl Anthony Schweiger
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Patent number: 6934931Abstract: A system and method for performing modeling, prediction, optimization, and control, including an enterprise wide framework for constructing modeling, optimization, and control solutions. The framework includes a plurality of base classes that may be used to create primitive software objects. These objects may then be combined to create optimization and/or control solutions. The distributed event-driven component architecture allows much greater flexibility and power in creating, deploying, and modifying modeling, optimization and control solutions. The system also includes various techniques for performing improved modeling, optimization, and control, as well as improved scheduling and control. For example, the system may include a combination of batch and continuous processing frameworks, and a unified hybrid modeling framework which allows encapsulation and composition of different model types, such as first principles models and empirical models.Type: GrantFiled: April 5, 2001Date of Patent: August 23, 2005Assignee: Pavilion Technologies, Inc.Inventors: Edward Stanley Plumer, Bijan Sayyar-Rodsari, Carl Anthony Schweiger, Ralph Bruce Ferguson, II, William Douglas Johnson, Celso Axelrud
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Publication number: 20010049595Abstract: A system and method for performing modeling, prediction, optimization, and control, including an enterprise wide framework for constructing modeling, optimization, and control solutions. The framework includes a plurality of base classes that may be used to create primitive software objects. These objects may then be combined to create optimization and/or control solutions. The distributed event-driven component architecture allows much greater flexibility and power in creating, deploying, and modifying modeling, optimization and control solutions. The system also includes various techniques for performing improved modeling, optimization, and control, as well as improved scheduling and control. For example, the system may include a combination of batch and continuous processing frameworks, and a unified hybrid modeling framework which allows encapsulation and composition of different model types, such as first principles models and empirical models.Type: ApplicationFiled: April 5, 2001Publication date: December 6, 2001Inventors: Edward Stanley Plumer, Bijan Sayyar-Rodsari, Carl Anthony Schweiger, Ralph Bruce Ferguson, William Douglas Johnson, Celso Axelrud