Rockwell Automation Technologies Incorporated

Pavilion Technologies, Inc.

Rockwell Automation, Inc.

Celso Axelrud

Kadir Liano

Eric J Hartman

Bijan Sayyarrodsari

Bijan Sayyar-Rodsari

Edward Stanley Plumer

Ralph Bruce Ferguson

Maina A Macharia

Michael Edward Tay

William Douglas Johnson

Carl Anthony Schweiger

Kidir Liano

Eric Hartman

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.

Parametric universal nonlinear dynamics approximator and use

In one embodiment, a model predictive control system for an industrial process includes a processor to execute an optimization module to determine manipulated variables for the process over a control horizon based on simulations performed using an objective function with an optimized process model and to control the process using the manipulated variables, to execute model modules including mathematical representations of a response or parameters of the process. The implementation details of the model modules are hidden from and inaccessible to the optimization module. The processor executes unified access modules (UAM). A first UAM interfaces between a first subset of the model modules and the optimization module and adapts output of the first subset for the optimization module, and a second UAM interfaces between a second subset of the model modules and the first subset and adapts output of the second subset for the first subset.

Optimization-based control with open modeling architecture systems and methods

A system and method are provided for integrated management of a biofuel distillation process and a biofuel dehydration process of a biofuel production process, comprising a dynamic multivariate model-based controller coupled to a dynamic multivariate predictive model. The model is executable to: receive distillation and dehydration process information including biofuel compositions, receive an objective for biofuel production output from the distillation and dehydration processes, e.g., target product composition, production rate, and/or feed rate, and generate model output comprising target values for a plurality of manipulated variables related to the distillation and dehydration processes in accordance with the objective. The controller is operable to dynamically control the biofuel production process by adjusting the plurality of manipulated variables to the model-determined target values in accordance with the objective for biofuel production.

Integrated model predictive control of distillation and dehydration sub-processes in a biofuel production process

Training a model of a non-linear process

The present invention provides a method for controlling nonlinear control problems within particle accelerators. This method involves first utilizing software tools to identify variable inputs and controlled variables associated with the particle accelerator, wherein at least one variable input parameter is a controlled variable. This software tool is further operable to determine relationships between the variable inputs and controlled variables. A control system that provides variable inputs to and acts on controller outputs from the software tools tunes one or more manipulated variables to achieve a desired controlled variable, which in the case of a particle accelerator may be realized as a more efficient collision.

Controlling a non-linear process with varying dynamics using non-linear model predictive control

System and method of applying adaptive control to the control of particle accelerators with varying dynamics behavioral characteristics using a nonlinear model predictive control technology

The present invention provides a method for controlling nonlinear process control problems. This method involves first utilizing modeling tools to identify variable inputs and controlled variables associated with the process, wherein at least one variable input is a manipulated variable. The modeling tools are further operable to determine relationships between the variable inputs and controlled variables. A control system that provides inputs to and acts on inputs from the modeling tools tunes one or more manipulated variable inputs to achieve a desired result like greater efficiency, higher quality, or greater consistency.

System and method of adaptive control of processes with varying dynamics

A system and method for performing modeling, prediction, optimization, and control, including an enterprise wide framework for constructing modeling, optimization, and control solutions. The framework includes a plurality of base classes that may be used to create primitive software objects. These objects may then be combined to create optimization and/or control solutions. The distributed event-driven component architecture allows much greater flexibility and power in creating, deploying, and modifying modeling, optimization and control solutions. The system also includes various techniques for performing improved modeling, optimization, and control, as well as improved scheduling and control. For example, the system may include a combination of batch and continuous processing frameworks, and a unified hybrid modeling framework which allows encapsulation and composition of different model types, such as first principles models and empirical models. The system further includes an integrated process scheduling solution referred to as process coordinator that seamlessly incorporates the capabilities of advanced control and execution into a real time event triggered optimal scheduling solution.

System and method for enterprise modeling, optimization and control

Growing community of inventors

Round Rock, TX, United States of America

Celso Axelrud