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    • 1. 发明授权
    • System and method for dynamic modeling, parameter estimation and optimization for processes having operating targets
    • 用于具有操作目标的过程的动态建模,参数估计和优化的系统和方法
    • US06816822B1
    • 2004-11-09
    • US09639543
    • 2000-08-16
    • Todd M. HessMartyn Glenn RichardsAndreas Kroll
    • Todd M. HessMartyn Glenn RichardsAndreas Kroll
    • G06F1710
    • G05B17/02G05B13/042G05B23/0227G05B23/0294
    • A technique to provide dynamic modeling, parameter estimation and optimization for processes having operating targets. The technique has an optimization cycle and a simulation cycle. The simulation cycle runs at a rate that is equal to or faster than the run rate of the optimization cycle. Both cycles obtain data from the field about the process. The optimization cycle provides a simulation execution of a dynamic process model and the results of that simulation are used to match the model response to the actual plant response. This matching determines if a dynamic parameter estimation and data reconciliation is needed for the model parameters. Optimized operating targets are determined using either the estimated and reconciled parameters and data or if the same is not needed the collected process data. Mathematical and heuristic assessment tools are used to determine if the optimized operating targets should or should not be invoked.
    • 为具有操作目标的过程提供动态建模,参数估计和优化的技术。 该技术具有优化周期和仿真周期。 仿真周期的运行速度等于或快于优化周期的运行速率。 两个循环都从该领域获取关于该过程的数据。 优化周期提供了动态过程模型的模拟执行,并且该模拟的结果用于将模型响应与实际工厂响应相匹配。 该匹配确定模型参数是否需要动态参数估计和数据调节。 优化的运营目标是使用估计和对帐的参数和数据确定的,或者如果收集的过程数据不需要相同的则确定。 数学和启发式评估工具用于确定是否应该调用优化的操作目标。
    • 5. 发明授权
    • System and methodology and adaptive, linear model predictive control based on rigorous, nonlinear process model
    • 基于严格的非线性过程模型的系统和方法学和自适应线性模型预测控制
    • US06826521B1
    • 2004-11-30
    • US09544390
    • 2000-04-06
    • Todd M. HessAndreas KrollCarl-Fredrik S. M. LindbergPer Erik ModénMikael PeterssonKenneth L. Praprost
    • Todd M. HessAndreas KrollCarl-Fredrik S. M. LindbergPer Erik ModénMikael PeterssonKenneth L. Praprost
    • G06G748
    • G05B13/048
    • A methodology for process modeling and control and the software system implementation of this methodology, which includes a rigorous, nonlinear process simulation model, the generation of appropriate linear models derived from the rigorous model, and an adaptive, linear model predictive controller (MPC) that utilizes the derived linear models. A state space, multivariable, model predictive controller (MPC) is the preferred choice for the MPC since the nonlinear simulation model is analytically translated into a set of linear state equations and thus simplifies the translation of the linearized simulation equations to the modeling format required by the controller. Various other MPC modeling forms such as transfer functions, impulse response coefficients, and step response coefficients may also be used. The methodology is very general in that any model predictive controller using one of the above modeling forms can be used as the controller. The methodology also includes various modules that improve reliability and performance. For example, there is a data pretreatment module used to pre-process the plant measurements for gross error detection. A data reconciliation and parameter estimation module is then used to correct for instrumentation errors and to adjust model parameters based on current operating conditions. The full-order state space model can be reduced by the order reduction module to obtain fewer states for the controller model. Automated MPC tuning is also provided to improve control performance.
    • 一种过程建模和控制的方法以及该方法的软件系统实现,其包括严格的非线性过程模拟模型,从严格模型导出的适当线性模型的生成,以及自适应线性模型预测控制器(MPC) 利用导出的线性模型。 状态空间,多变量,模型预测控制器(MPC)是MPC的首选,因为非线性仿真模型被分析地转换成一组线性状态方程,从而简化了线性化仿真方程到所需的建模格式的转换 控制器。 还可以使用各种其它MPC建模形式,例如传递函数,脉冲响应系数和阶跃响应系数。 该方法是非常普遍的,因为使用上述建模形式之一的任何模型预测控制器都可以用作控制器。 该方法还包括提高可靠性和性能的各种模块。 例如,有一个数据预处理模块用于预处理粗差错检测的工厂测量。 然后使用数据调节和参数估计模块来校正仪器错误,并根据当前操作条件调整模型参数。 可以通过订单减少模块来减少全阶状态空间模型,以获得更少的控制器模型的状态。 还提供自动MPC调谐以提高控制性能。