Fisher-rosemount Systems, Inc

Peter Wojsznis

Terrence L Blevins

Wilhelm K Wojsznis

Mark J Nixon

John Mark Caldwell

Ashish Mehta

Marty James Lewis

David R Denison

Gregory K McMillan

Larry Oscar Jundt

Dirk Thiele

Nathan W Pettus

Ron Ottenbacher

A method of controlling and managing a process control system having a plurality of control loops includes implementing a plurality of control routines to control operation of the plurality of control loops, respectively, wherein the control routines may include at least one non-adaptive control routine. The method then collects operating condition data in connection with the operation of each control loop, and identifies a respective process model for each control loop from the respective operating condition data collected for each control loop. The identification of the respective process models may be automatic as a result of a detected process change or may be on-demand as a result of an injected parameter change. The process models are then analyzed to measure or determine the operation of the process control loops.

Process model identification in a process control system

A method of diagnosing an adaptive process control loop includes measuring process control loop signal data, generating a plurality of process control loop parameters from the process loop signal data and evaluating a condition of the adaptive process control loop from one or more of the plurality of process control loop parameters. The process control loop data is generated as a result of a normal operation of one or more process control devices within the adaptive process control loop when the adaptive process control loop is connected on-line within a process control environment. A self-diagnostic process control loop includes a diagnostic tool adapted to receive a diagnostic index pertaining to a process control loop parameter for a plurality of components of the process control loop and for the complete process control loop. Each diagnostic index is generated from signal data by a corresponding index computation tool. The diagnostic tool is further adapted to evaluate a condition of the process control loop from one or more of the diagnostic indices.

Self-diagnostic process control loop for a process plant

A method of controlling a process control system having a plurality of control loops includes collecting operating condition data in connection with the operation of each control loop of the plurality of control loops, identifying a respective process model for each control loop of the plurality of control loops from the respective operating condition data collected for each control loop of the plurality of control loops and developing a process model history by storing data indicative of the multiple identified process models for each control loop of the plurality of control loops.

A method of diagnosing an adaptive process control loop includes measuring process control loop signal data, generating a plurality of process control loop parameters from the process loop signal data and evaluating a condition of the adaptive process control loop from one or more of the plurality of process control loop parameters. The process control loop data is generated as a result of a normal operation of one or more process control devices within the adaptive process control loop when the adaptive process control loop is connected on-line within a process control environment. A self-diagnostic process control loop includes a diagnostic tool adapted to receive a diagnostic index pertaining to a process control loop parameter for each component of the process control loop and for the complete process control loop. Each diagnostic index is generated from signal data by a corresponding index computation tool. The diagnostic tool is further adapted to evaluate a condition of the process control loop from one or more of the diagnostic indices.

An adaptive process controller performs continuously scheduled process model parameter interpolation to determine a particular set of process model parameters which are used to develop controller tuning parameters for controller tuning. More particularly, a state-based, adaptive PID controller described herein uses a new technique to determine an appropriate process model to be used to perform adaptive tuning over the various operating regions of the plant, and in particular, uses a process model parameter determination technique that enables continuously scheduled process model parameter update over the various plant operating regions or points. The use of this continuously scheduled process model parameter update method provides for smoother transitions between tuning parameters used in the PID controller during adaptive tuning procedures which are implemented based on changes in the operating region or the operating point of the process, thereby providing for better overall control.

Continuously scheduled model parameter based adaptive controller

A system and method for controlling a process includes simulating the process and producing a simulated output of the process, developing a set of target values based on measured inputs from the process and based on the simulated output from the process simulator, and producing multiple control outputs configured to control the process based on the set of target values during each operational cycle of the process control system. The simulated outputs include one or more predicted future values up to the steady state of the process.

Multi-objective predictive process optimization with concurrent process simulation

A process control system integrates the collection and analysis of process control data used to perform certain computationally expensive process control functions, like adaptive model generation and tuning parameter generation, in the same control device in which one or more of the process control routines are implemented, to thereby provide for faster and more efficient support of the process control routines. This system replaces a layered approach using multiple processing devices by integrating an analytical server which performs computationally expensive analyses used by one or more control routines directly into the real-time control device in which the one or more control routines are located. This integration provides the ability to analyze large quantities of data for multiple process loops controlled by a particular device in a fast and efficient manner.

Analytical server integrated in a process control network

A controller includes a control module to control operation of a process in response to control data, a plug-in module coupled to the control module as a non-layered, integrated extension thereof, and a model identification engine. The plug-in detects a change in the control data, and a collects the control data and data in connection with a condition of the process in response to the detected change. The model identification engine executes a plurality of model parameter identification cycles. Each cycle includes simulations of the process each having different simulation parameter values and each using the control data as an input, an estimation error calculation for each simulation based on an output of the simulation and based on the operating condition data, and a calculation of a model parameter value based on the estimation errors and simulation parameter values used in the simulation corresponding to each of the estimation errors.

Method and apparatus for intelligent control and monitoring in a process control system

Disclosed is a method of controlling and managing a process control system having a plurality of control loops. The method includes implementing a plurality of control routines to control operation of the plurality of control loops, respectively. The plurality of control routines may include at least one non-adaptive control routine. Operating condition data is then collected in connection with the operation of each control loop of the plurality of control loops, and a respective process model is identified for each control loop of the plurality of control loops from the respective operating condition data collected for each control loop of the plurality of control loops. In some embodiments, the identification of the respective process models may be automatic as a result of a detected process change or be on-demand as a result of an injected parameter change.

An optimization technique for use in driving a process plant controller, such as a model predictive controller, uses an organized, systematic but computationally simple method of relaxing or redefining manipulated, control and/or auxiliary variable constraints when there is no feasible optimal solution within pre-established constraints, to thereby develop an achievable solution for use by the controller. The optimization routine uses penalized slack variables and/or redefines the constraint model in conjunction with the use of penalty variables to develop a new objective function, and then uses the new objective function to determine a control solution that bests meets the original constraint limits.

Constraint and limit feasibility handling in a process control system optimizer

An adaptive multivariable process control system includes a multivariable process controller, such as a model predictive controller, having a multivariable process model characterized as a set of two or more single-input, single-output (SISO) models and an adaptation system which adapts the multivariable process model. The adaptation system detects changes in process inputs sufficient to start an adaptation cycle and, when such changes are detected, collects process input and output data needed to perform model adaptation. The adaptation system next determines a subset of the SISO models within the multivariable process model which are to be adapted, based on, for example, a determination of which process inputs are most correlated with the error between the actual (measured) process output and the process output developed by the multivariable process model. The adaptation system then performs standard or known model switching and parameter interpolation techniques to adapt each of the selected SISO models. After the adaptation of one or more of the SISO models, the resulting multivariable process model is validated by determining if the adapted multivariable process model has lower modeling error than the current multivariable process model. If so, the adapted multivariable process model is used in the multivariable controller.

Adaptive multivariable process controller using model switching and attribute interpolation

A process control configuration system is provided for use in creating or viewing an integrated optimization and control block that implements an optimization routine and a multiple-input/multiple-output control routine. The configuration system may enable a user to display or configure the optimizer or the control routine. A storage routine may store information pertaining to a plurality of control and auxiliary variables and to a plurality of manipulated variables to be used by the optimization routine and/or the control routine. A display routine may present a display to a user regarding the information pertaining to the plurality of control and auxiliary variables and to the plurality of manipulated variables.

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Cedar Park, TX, United States of America

Peter Wojsznis