EMPIRICAL MODELING WITH GLOBALLY ENFORCED GENERAL CONSTRAINTS
    4.
    发明申请
    EMPIRICAL MODELING WITH GLOBALLY ENFORCED GENERAL CONSTRAINTS 审中-公开
    具有全球强制性一般约束的实践建模

    公开(公告)号:US20160018795A1

    公开(公告)日:2016-01-21

    申请号:US14868022

    申请日:2015-09-28

    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。

    Integrated optimization and control for production plants

    公开(公告)号:US10067485B2

    公开(公告)日:2018-09-04

    申请号:US14860367

    申请日:2015-09-21

    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.

    Empirical modeling with globally enforced general constraints
    6.
    发明授权
    Empirical modeling with globally enforced general constraints 有权
    经验模型与全球强制性一般约束

    公开(公告)号:US09147153B2

    公开(公告)日:2015-09-29

    申请号:US13670187

    申请日:2012-11-06

    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。

    INTEGRATED OPTIMIZATION AND CONTROL FOR PRODUCTION PLANTS
    9.
    发明申请
    INTEGRATED OPTIMIZATION AND CONTROL FOR PRODUCTION PLANTS 审中-公开
    综合优化和控制生产植物

    公开(公告)号:US20160011572A1

    公开(公告)日:2016-01-14

    申请号:US14860367

    申请日:2015-09-21

    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。

    EMPIRICAL MODELING WITH GLOBALLY ENFORCED GENERAL CONSTRAINTS
    10.
    发明申请
    EMPIRICAL MODELING WITH GLOBALLY ENFORCED GENERAL CONSTRAINTS 有权
    具有全球强制性一般约束的实践建模

    公开(公告)号:US20140129491A1

    公开(公告)日:2014-05-08

    申请号:US13670187

    申请日:2012-11-06

    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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