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    • 3. 发明授权
    • Method for on-line optimization of a plant
    • 一种植物在线优化方法
    • US06278899B1
    • 2001-08-21
    • US09167504
    • 1998-10-06
    • Stephen PicheJohn P. HavenerDonald Semrad
    • Stephen PicheJohn P. HavenerDonald Semrad
    • G05B1302
    • G05B17/02G05B13/048
    • An on-line optimizer is comprised of a nonlinear dynamic model (702) which is operable to provide an estimation of the output of a plant. This receives manipulated variables (MV), disturbance variables (DV), and computed disturbance variables (CDB). The estimated output of the model is then compared to the actual output measured by virtual on-line analyzer (VOA) (616). This is compared is a difference block 618 to generate a bias which is then filtered by a filter(620). The output thereof is then provided to an output block (672) in a steady state optimizer (700) to offset the desired setpoints. These set points are input to a steady state nonlinear model which is operable to optimize the inputs to the plants for use for writing new set points in accordance with a predetermined cost function. This cost function is utilized to optimize the new inputs with the use of the steady state model in accordance with various constraints and target values.
    • 在线优化器由非线性动力学模型(702)组成,其可操作以提供对植物的输出的估计。 这接收到操纵变量(MV),干扰变量(DV)和计算的干扰变量(CDB)。 然后将模型的估计输出与虚拟在线分析仪(VOA)测量的实际输出进行比较(616)。 比较差异块618以产生偏差,然后由滤波器(620)滤波。 然后,其输出被提供给稳态优化器(700)中的输出块(672)以偏移所需的设定点。 这些设定点被输入到稳态非线性模型,该稳态非线性模型可操作以根据预定的成本函数优化输入到工厂用于写入新的设定点。 该成本函数用于根据各种约束和目标值利用稳态模型来优化新的输入。
    • 5. 发明授权
    • Dynamic controller for controlling a system
    • 用于控制系统的动态控制器
    • US07050866B2
    • 2006-05-23
    • US10847211
    • 2004-05-17
    • Gregory D. MartinEugene BoeStephen PicheJames David KeelerDouglas TimmerMark GerulesJohn P. Havener
    • Gregory D. MartinEugene BoeStephen PicheJames David KeelerDouglas TimmerMark GerulesJohn P. Havener
    • G05B13/02
    • G05B17/02G05B13/048
    • 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. Additionally, the path that is traversed between steady-state value changes.
    • 在预测,控制和优化环境中提供独立的静态和动态模型的方法使用独立的静态模型(20)和独立的动态模型(22)。 静态模型(20)是一种严格的预测模型,可在广泛的数据范围内进行训练,而动态模型(22)则是在窄范围的数据上进行训练。 使用静态模型(20)的增益K来缩放动态模型(22)的增益k。 被称为变量的模型(22)的强制动态部分通过增益K和k的比率来缩放。 b)对动态模型(22)的增益有直接的影响。 这通过系数修改块(40)来促进。 此后,将输入到静态模型(20)的新值与先前稳态值之间的差用作动态模型(22)的输入。 然后将预测的动态输出与先前的稳态值相加以提供预测值Y.此外,在稳态值变化之间经过的路径。
    • 10. 发明授权
    • Method and apparatus for modeling dynamic and steady-state processes for prediction, control and optimization
    • 用于预测,控制和优化的动态和稳态过程建模的方法和装置
    • US06487459B1
    • 2002-11-26
    • US09250432
    • 1999-02-16
    • Gregory D. MartinEugene BoeStephen PicheJames David KeelerDouglas TimmerMark GerulesJohn P. Havener
    • Gregory D. MartinEugene BoeStephen PicheJames David KeelerDouglas TimmerMark GerulesJohn P. Havener
    • G05B1302
    • G05B17/02G05B13/048
    • 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. Additionally, the path that is traversed between steady-state value changes.
    • 在预测,控制和优化环境中提供独立的静态和动态模型的方法使用独立的静态模型(20)和独立的动态模型(22)。 静态模型(20)是一种严格的预测模型,可在广泛的数据范围内进行训练,而动态模型(22)则是在窄范围的数据上进行训练。 使用静态模型(20)的增益K来缩放动态模型(22)的增益k。 被称为双变量的模型(22)的强制动态部分通过增益K和k的比率来缩放。 bi对动态模型的获得有直接的影响(22)。 这通过系数修改块(40)来促进。 此后,将输入到静态模型(20)的新值与先前稳态值之间的差用作动态模型(22)的输入。 然后将预测的动态输出与先前的稳态值相加以提供预测值Y.此外,在稳态值变化之间经过的路径。