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41. 发明申请

US20060100720A1 Kiln free lime control 有权
标题翻译：窑炉免费石灰控制
公开(公告)号：US20060100720A1
公开(公告)日：2006-05-11
申请号：US11317499
申请日：2005-12-22
申请人： Gregory Martin , Eugene Boe , Stephen Piche , James Keeler , Douglas Timmer , Mark Gerules , John Havener , Steven McGarel
发明人： Gregory Martin , Eugene Boe , Stephen Piche , James Keeler , Douglas Timmer , Mark Gerules , John Havener , Steven McGarel
IPC分类号： G05B13/02
CPC分类号： G05B13/048 , G05B13/042 , G05B17/02
摘要： A kiln thermal and combustion control. A predictive model is provided of the dynamics of selected aspects of the operation of the system for modeling the dynamics thereof. The model has at least two discrete models associated therewith that model at least two of the selected aspects, the at least two discrete models having different dynamic responses. An optimizer receives desired values for the selected aspects of the operation of the system modeled by the model and optimizes the inputs to the model to minimize error between the predicted and desired values. A control input device then applies the optimized input values to the system after optimization thereof.
摘要翻译：窑炉和燃烧控制。提供了用于对其动力学进行建模的系统的操作的选定方面的动态的预测模型。所述模型具有至少两个与之相关的离散模型，所述至少两个所选择的方面模型，所述至少两个离散模型具有不同的动态响应。优化器接收由模型建模的系统的操作的所选方面的期望值，并优化对模型的输入以最小化预测值和期望值之间的误差。然后，控制输入装置优化后对系统施加优化的输入值。

42. 发明申请

US20060074501A1 Method and apparatus for training a system model with gain constraints 有权
标题翻译：用于训练具有增益约束的系统模型的方法和装置
公开(公告)号：US20060074501A1
公开(公告)日：2006-04-06
申请号：US11267812
申请日：2005-11-04
申请人： Eric Hartman , Stephen Piche , Mark Gerules
发明人： Eric Hartman , Stephen Piche , Mark Gerules
IPC分类号： G05B13/02
CPC分类号： G06N3/02 , G05B13/027 , G05B13/042 , G05B13/048 , G05B17/02
摘要： Method and apparatus for training a system model with gain constraints. A method is disclosed for training a steady-state model, the model having an input and an output and a mapping layer for mapping the input to the output through a stored representation of a system. A training data set is provided having a set of input data u(t) and target output data y(t) representative of the operation of a system. The model is trained with a predetermined training algorithm which is constrained to maintain the sensitivity of the output with respect to the input substantially within user defined constraint bounds by iteratively minimizing an objective function as a function of a data objective and a constraint objective. The data objective has a data fitting learning rate and the constraint objective has constraint learning rate that are varied as a function of the values of the data objective and the constraint objective after selective iterative steps.
摘要翻译：用于训练具有增益约束的系统模型的方法和装置。公开了一种用于训练稳态模型的方法，该模型具有输入和输出以及用于通过存储的系统表示将输入映射到输出的映射层。提供具有代表系统的操作的一组输入数据u（t）和目标输出数据y（t）的训练数据集。用预定的训练算法来训练该模型，该训练算法被约束以通过将作为数据目标和约束目标的函数的目标函数迭代地最小化来维持相对于基本上在用户定义的约束边界内的输入的输出的灵敏度。数据目标具有数据拟合学习率，并且约束目标具有约束学习速率，其作为数据目标的值和选择性迭代步骤之后的约束目标的函数而变化。

43. 发明申请

US20060045804A1 Process parameter estimation in controlling emission of a non-particulate pollutant into the air 有权
标题翻译：控制空气中非微粒污染物排放的过程参数估计
公开(公告)号：US20060045804A1
公开(公告)日：2006-03-02
申请号：US11003336
申请日：2004-12-06
申请人： Scott Boyden , Stephen Piche
发明人： Scott Boyden , Stephen Piche
IPC分类号： G05D7/00
CPC分类号： G05B13/027 , B01D2257/302 , B01D2257/404 , G05B13/048 , Y10T436/12
摘要： A parameter value estimator is provided for a process performed primarily to control emission of a particular non-particulate pollutant, such as NOx and SO2, into the air. The process has multiple process parameters (MPPs) including a parameter representing an amount of the particular non-particulate pollutant emitted. The parameter value estimator includes either a neural network process model or a non-neural network process model. In either case the model represents a relationship between one of the MPPs, other than the parameter representing the amount of the emitted particular non-particulate pollutant, and one or more other of the MPPs. Also included is a processor configured with the logic, e.g. programmed software, to estimate a value of the one MPP based on a value of each of the one or more other MPPs and the one model.
摘要翻译：为主要用于控制特定非微粒污染物（例如NO x和SO 2）的排放进入空气的过程提供了参数值估计器。该过程具有多个过程参数（MPP），其包括表示特定非微粒污染物排放量的参数。参数值估计器包括神经网络过程模型或非神经网络过程模型。在任一情况下，该模型表示MPP之间的关系，而不是表示所发射的特定非微粒污染物的量的参数以及MPP中的一个或多个另外的MPP之间的关系。还包括配置有逻辑的处理器，例如基于一个或多个其他MPP和一个模型中的每一个的值来估计一个MPP的值。

44. 发明申请

US20060045803A1 APC process control when process parameters are inaccurately measured 有权
标题翻译：过程参数不准确测量时的APC过程控制
公开(公告)号：US20060045803A1
公开(公告)日：2006-03-02
申请号：US11002439
申请日：2004-12-03
申请人： Scott Boyden , Stephen Piche
发明人： Scott Boyden , Stephen Piche
IPC分类号： G05D7/00
CPC分类号： G05B13/027 , B01D2257/302 , B01D2257/404 , G05B13/048 , Y10T436/12
摘要： A controller is provided for directing control of a process performed to control an amount of a pollutant emitted into the air. The process has multiple process parameters (MPPs) The controller includes either a neural network process model or a non-neural network process model. Whichever type model is included, it will represent a relationship between one of the MPPs and other of the MPPs. The controller also includes a control processor having the logic to determine the validity of a measured value of the one MPP based on the one model. The control processor directs control of the process in accordance with the measured value of the one MPP only if the measured value of the one MPP is determined to be valid. On the other hand, if the measured value is determined to be invalid, the control processor may direct control of the process in accordance with an estimated value of the one MPP.
摘要翻译：提供控制器用于引导控制执行的处理以控制排放到空气中的污染物的量。该过程具有多个过程参数（MPP）。控制器包括神经网络过程模型或非神经网络过程模型。无论哪种类型的模型都包含在内，它将代表一个MPP和其他MPP之间的关系。该控制器还包括控制处理器，该控制处理器具有基于一个模型来确定一个MPP的测量值的有效性的逻辑。只有当一个MPP的测量值被确定为有效时，控制处理器才根据一个MPP的测量值来指导该过程的控制。另一方面，如果确定测量值无效，则控制处理器可以根据一个MPP的估计值来直接控制处理。

45. 发明授权

US09329582B2 Method and apparatus for minimizing error in dynamic and steady-state processes for prediction, control, and optimization 有权
标题翻译：用于最小化用于预测，控制和优化的动态和稳态过程中的误差的方法和装置
公开(公告)号：US09329582B2
公开(公告)日：2016-05-03
申请号：US13608578
申请日：2012-09-10
申请人： Eugene Boe , Stephen Piche , Gregory D. Martin
发明人： Eugene Boe , Stephen Piche , Gregory D. Martin
IPC分类号： G05B9/02 , G05B13/02 , G05B13/04 , G05B17/02
CPC分类号： G05B13/027 , G05B13/042 , G05B13/048 , G05B17/02 , G09B23/02
摘要： 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. 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.
摘要翻译：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。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.

46. 发明授权

US08340824B2 Sootblowing optimization for improved boiler performance 有权
标题翻译：吹灰优化，以提高锅炉性能
公开(公告)号：US08340824B2
公开(公告)日：2012-12-25
申请号：US11868021
申请日：2007-10-05
申请人： John Robert James , John McDermott , Stephen Piche , Fred Pickard , Neel J. Parikh
发明人： John Robert James , John McDermott , Stephen Piche , Fred Pickard , Neel J. Parikh
IPC分类号： G05B13/02
CPC分类号： F22B37/48 , F22B37/56 , F23J3/023 , Y10T436/12
摘要： A sootblowing control system that uses predictive models to bridge the gap between sootblower operation and boiler performance goals. The system uses predictive modeling and heuristics (rules) associated with different zones in a boiler to determine an optimal sequence of sootblower operations and achieve boiler performance targets. The system performs the sootblower optimization while observing any operational constraints placed on the sootblowers.
摘要翻译：一个吹灰控制系统，使用预测模型来弥合吹灰器操作和锅炉性能目标之间的差距。该系统使用与锅炉中不同区域相关联的预测建模和启发式（规则）来确定吹灰器操作的最佳顺序并实现锅炉性能目标。系统执行吹灰器优化，同时观察放置在吹灰器上的任何操作限制。

47. 发明授权

US08311673B2 Method and apparatus for minimizing error in dynamic and steady-state processes for prediction, control, and optimization 有权
标题翻译：用于最小化用于预测，控制和优化的动态和稳态过程中的误差的方法和装置
公开(公告)号：US08311673B2
公开(公告)日：2012-11-13
申请号：US11359296
申请日：2006-02-21
申请人： Eugene Boe , Stephen Piche , Gregory D. Martin
发明人： Eugene Boe , Stephen Piche , Gregory D. Martin
IPC分类号： G05B19/04 , G06F19/00
CPC分类号： G05B13/027 , G05B13/042 , G05B13/048 , G05B17/02 , G09B23/02
摘要： 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. 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.
摘要翻译：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。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.

48. 发明授权

US08295953B2 System for optimizing power generating unit 有权
标题翻译：优化发电机组的系统
公开(公告)号：US08295953B2
公开(公告)日：2012-10-23
申请号：US13293568
申请日：2011-11-10
申请人： Stephen Piche
发明人： Stephen Piche
IPC分类号： G05B13/02
CPC分类号： H02J3/38 , F23N2023/40 , F23N2023/44 , H02J2003/007 , Y02E40/76 , Y02E60/76 , Y04S10/545 , Y04S40/22
摘要： A method and apparatus for optimizing the operation of a single or multiple power generating units using advanced optimization, modeling, and control techniques. In one embodiment, a plurality of component optimization systems for optimizing power generating unit components are sequentially coordinated to allow optimized values determined by a first component optimization system to be fed forward for use as an input value to a subsequent component optimization system. A unit optimization system may be provided to determine goals and constraints for the plurality of component optimization systems in accordance with economic data. In one embodiment of the invention, a multi-unit optimization system is provided to determine goals and constraints for component optimization systems of different power generating units. Both steady state and dynamic models are used for optimization.
摘要翻译：一种使用先进优化，建模和控制技术优化单个或多个发电机组的运行的方法和装置。在一个实施例中，用于优化功率产生单元组件的多个组件优化系统被顺序地协调，以允许由第一组件优化系统确定的优化值被提前向用作后续组件优化系统的输入值。可以提供单元优化系统以根据经济数据确定多个部件优化系统的目标和约束。在本发明的一个实施例中，提供多单元优化系统以确定不同发电单元的部件优化系统的目标和约束。稳态和动态模型均用于优化。

49. 发明申请

US20110104015A1 APC PROCESS PARAMETER ESTIMATION 有权
标题翻译： APC过程参数估计
公开(公告)号：US20110104015A1
公开(公告)日：2011-05-05
申请号：US12917985
申请日：2010-11-02
申请人： Scott A. BOYDEN , Stephen Piche
发明人： Scott A. BOYDEN , Stephen Piche
IPC分类号： B01D53/48 , B01D53/56 , G06N3/02
CPC分类号： G05B13/027 , B01D2257/302 , B01D2257/404 , G05B13/048 , Y10T436/12
摘要： A virtual analyzer is provided to estimate either an attribute of a reactant applied during performance of, or an amount of a reactant exhausted by, a process having multiple process parameters (MPPs) that is performed to control an amount of a pollutant emitted into the air. The virtual analyzer includes an interface which receives signals corresponding to attributes of the MPPs. If the process is a wet flue gas desulfurization (WFGD) process, the signals include a signal corresponding to a measured pH level of the applied reactant. If the process is a selective catalytic reduction (SCR) process, the signals include a signal corresponding to a measured amount of the reactant exhausted by the process.
摘要翻译：提供虚拟分析器以估计在执行过程中施加的反应物的属性，或由具有多个处理参数（MPP）的过程所排出的反应物的量的属性，所述过程参数（MPP）被执行以控制排放到空气中的污染物的量。虚拟分析器包括接收与MPP的属性对应的信号的接口。如果该方法是湿式烟道气脱硫（WFGD）方法，信号包括对应于施加的反应物的测量的pH值的信号。如果该方法是选择性催化还原（SCR）方法，信号包括对应于由该方法排出的反应物的测量量的信号。

50. 发明授权

US07860586B2 Process parameter estimation in controlling emission of a non-particulate pollutant into the air 有权
标题翻译：控制空气中非微粒污染物排放的过程参数估计
公开(公告)号：US07860586B2
公开(公告)日：2010-12-28
申请号：US11003336
申请日：2004-12-06
申请人： Scott A. Boyden , Stephen Piche
发明人： Scott A. Boyden , Stephen Piche
IPC分类号： G05B13/02 , G05B21/00 , G06F7/00 , G06F17/00 , F01N3/00 , G01N30/96 , B01D50/00 , B01D53/34 , F23J11/00 , B01D53/86 , C01B21/00 , C01B17/00
CPC分类号： G05B13/027 , B01D2257/302 , B01D2257/404 , G05B13/048 , Y10T436/12
摘要： A parameter value estimator is provided for a process performed primarily to control emission of a particular non-particulate pollutant, such as NOx and SO2, into the air. The process has multiple process parameters (MPPs) including a parameter representing an amount of the particular non-particulate pollutant emitted. The parameter value estimator includes either a neural network process model or a non-neural network process model. In either case the model represents a relationship between one of the MPPs, other than the parameter representing the amount of the emitted particular non-particulate pollutant, and one or more other of the MPPs. Also included is a processor configured with the logic, e.g. programmed software, to estimate a value of the one MPP based on a value of each of the one or more other MPPs and the one model.
摘要翻译：为主要用于控制特定非微粒污染物（如NOx和SO2）排放到空气中的过程提供参数值估计器。该过程具有多个过程参数（MPP），其包括表示特定非微粒污染物排放量的参数。参数值估计器包括神经网络过程模型或非神经网络过程模型。在任一情况下，该模型表示MPP之间的关系，而不是表示所发射的特定非微粒污染物的量的参数以及MPP中的一个或多个另外的MPP之间的关系。还包括配置有逻辑的处理器，例如基于一个或多个其他MPP和一个模型中的每一个的值来估计一个MPP的值。

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