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公开(公告)号：US20150185717A1
公开(公告)日：2015-07-02
申请号：US14659003
申请日：2015-03-16
Applicant: ROCKWELL AUTOMATION TECHNOLOGIES, INC.
Inventor： Bijan Sayyar-Rodsari , Edward S. Plumer , Eric Hartman , Celso Axelrud , Kadir Liano
IPC: G05B13/04
CPC classification number: G05B13/048 , G05B13/042 , G05B17/02
Abstract: System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints.
Abstract translation: 用于建模非线性过程的系统和方法。 用于非线性过程的预测优化或控制的组合模型包括耦合到参数化动态或静态模型的非线性近似器，可操作以对非线性过程建模。 非线性近似器接收过程输入，并为参数化动态模型生成参数。 参数化动态模型接收参数和过程输入，并根据参数和过程输入生成预测过程输出，其中预测过程输出可用于分析和/或控制非线性过程。 组合模型可以通过识别过程输入和输出（I / O），收集过程I / O的数据，确定来自先验知识的模型行为的约束，制定优化问题，以基本上同时的方式进行训练， 执行优化算法以确定受限于确定的模型参数，并验证模型与约束的一致性。
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公开(公告)号：US11675319B2
公开(公告)日：2023-06-13
申请号：US16934867
申请日：2020-07-21
Applicant: Rockwell Automation Technologies, Inc.
Inventor： Bijan Sayyar-Rodsari , Eric Jon Hartman , Carl Anthony Schweiger
Abstract: 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.
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公开(公告)号：US20200348630A1
公开(公告)日：2020-11-05
申请号：US16934867
申请日：2020-07-21
Applicant: Rockwell Automation Technologies, Inc.
Inventor： Bijan Sayyar-Rodsari , Eric Jon Hartman , Carl Anthony Schweiger
Abstract: 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.
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公开(公告)号：US12164271B2
公开(公告)日：2024-12-10
申请号：US18312797
申请日：2023-05-05
Applicant: Rockwell Automation Technologies, Inc.
Inventor： Bijan Sayyar-Rodsari , Eric Jon Hartman , Carl Anthony Schweiger
Abstract: 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.
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公开(公告)号：US10739735B2
公开(公告)日：2020-08-11
申请号：US14868022
申请日：2015-09-28
Applicant: ROCKWELL AUTOMATION TECHNOLOGIES, INC.
Inventor： Bijan Sayyar-Rodsari , Eric Jon Hartman , Carl Anthony Schweiger
Abstract: 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.
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Patent Agency Ranking
公开(公告)号：US20160011572A1
公开(公告)日：2016-01-14
申请号：US14860367
申请日：2015-09-21
Applicant: ROCKWELL AUTOMATION TECHNOLOGIES, INC.
Inventor： Bijan Sayyar-Rodsari , Carl Anthony Schweiger
IPC: G05B13/04
CPC classification number: G05B13/04
Abstract: 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. In particular, the parametric multifaceted models may convert the first set of parameters into the second set of parameters in a time horizon allowing for control of the process plant by a control system based on the second set of parameters.
Abstract translation: 本发明提供使用参数多方面模型来优化和控制生产设备的新技术。 特别地，参数多面模型可以被配置为将与生产设备相关的第一组参数（例如，控制参数）转换为与生产设备相关的第二组参数（例如，优化参数）。 一般来说，第一组参数将与第二组参数不同。 例如，第一组参数可以指示低级的实时控制参数，并且第二组参数可以指示高级的经济参数。 使用适当的参数化可以允许参数多面模型在合理的时间内提供生产工厂的适当水平的细节。 特别地，参数多方面模型可以在时间范围内将第一组参数转换为第二组参数，从而允许由控制系统基于第二组参数控制过程工厂。
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公开(公告)号：US20140129491A1
公开(公告)日：2014-05-08
申请号：US13670187
申请日：2012-11-06
Applicant: Vesta Garcia , ROCKWELL AUTOMATION TECHNOLOGIES, INC.
Inventor： Bijan Sayyar-Rodsari , Eric Jon Hartman , Carl Anthony Schweiger
IPC: G06N99/00
CPC classification number: G05B13/041 , G06F17/10 , G06N3/02 , G06N99/005
Abstract: 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.
Abstract translation: 在某些实施例中，一种方法包括制定优化问题以确定要建模的系统的多个模型参数。 该方法还包括解决优化问题来定义系统的经验模型。 该方法还包括使用训练数据训练经验模型。 经验模型通过与系统的第一原则信息和过程知识相关的一般约束来约束。
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公开(公告)号：US11169494B2
公开(公告)日：2021-11-09
申请号：US14659003
申请日：2015-03-16
Applicant: ROCKWELL AUTOMATION TECHNOLOGIES, INC.
Inventor： Bijan Sayyar-Rodsari , Edward S Plumer , Eric Hartman , Celso Axelrud , Kadir Liano
Abstract: System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints.
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公开(公告)号：US10067485B2
公开(公告)日：2018-09-04
申请号：US14860367
申请日：2015-09-21
Applicant: ROCKWELL AUTOMATION TECHNOLOGIES, INC.
Inventor： Bijan Sayyar-Rodsari , Carl Anthony Schweiger
IPC: G05B13/04
Abstract: 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. In particular, the parametric multifaceted models may convert the first set of parameters into the second set of parameters in a time horizon allowing for control of the process plant by a control system based on the second set of parameters.
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公开(公告)号：US09147153B2
公开(公告)日：2015-09-29
申请号：US13670187
申请日：2012-11-06
Applicant: ROCKWELL AUTOMATION TECHNOLOGIES, INC.
Inventor： Bijan Sayyar-Rodsari , Eric Jon Hartman , Carl Anthony Schweiger
CPC classification number: G05B13/041 , G06F17/10 , G06N3/02 , G06N99/005
Abstract: 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.
Abstract translation: 在某些实施例中，一种方法包括制定优化问题以确定要建模的系统的多个模型参数。 该方法还包括解决优化问题来定义系统的经验模型。 该方法还包括使用训练数据训练经验模型。 经验模型通过与系统的第一原则信息和过程知识相关的一般约束来约束。
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