Fisher-Rosemount Systems, Inc

En Solución, Inc.

Dirk Thiele

Wilhelm K Wojsznis

Terrence L Blevins

Mark J Nixon

Noel Howard Bell

Ashish Mehta

Ron Ottenbacher

Shaobo Qiu

Joel Nicholas Ohmart

Robert Burke Havekost

Peter Wojsznis

Christopher J Worek

Tom Aneweer

William E Bennett

Vasiliki Tzovla

Terry Blevins

John A Gudaz

Alex Edward Athey

Brian Crandall

Jessica Siu

Forrest Stallings

Laura Milena Ramirez Rojas

A system may include a nanobubble generator that uses a shearing force applied by a fluid received through a fluid inlet and a negative pressure applied to an outlet by a pump to provide a vacuum-assisted shear flow nanobubble generator system. In some implementations, the system may include a nanobubble generator including a porous component including a chamber coupled to receive a gas and including a surface having a plurality of gas-permeable openings. The nanobubble generator may include an inlet and an outlet on opposing sides of the porous component to direct the fluid across the openings. The system may include a pump to apply a negative pressure to the outlet of the nanobubble generator. The negative fluid pressure and the fluid flow across the openings cooperate to form nanobubbles at low injected gas pressures, increasing the efficiency of production of nanobubble solutions with pressure sensitive gasses, such as ozone.

Shear flow nanobubble generator

Systems and methods for automated commissioning of virtualized distributed control systems are disclosed. An example method includes accessing a data structure including a list of configuration names for network cards associated with first and second host servers of a virtual process control environment. The first and second host servers implement virtual machines corresponding to workstations for a process control system. The example method also includes when configuring the first host server, assigning a first name to a first one of the network cards associated with the first host server. The example method further includes when configuring the second host server, assigning the first name to a second one of the network cards associated with the second host server based on a user selection of the first name from the list of configuration names. The second host server is configured after the first host server.

Systems and methods for automated commissioning of virtualized distributed control systems

An on-line data analytics device can be installed in a process control system as a standalone device that operates in parallel with, but non-intrusively with respect to, the on-line control system to perform on-line analytics for a process without requiring the process control system to be reconfigured or recertified. The data analytics device includes a data analytics engine coupled to a logic engine that receives process data collected from the process control system in a non-intrusive manner. The logic engine operates to determine further process variable values not generated within the process control system and provides the collected process variable data and the further process variable values to the data analytics engine. The data analytics engine executes statistically based process models, such as batch models, stage models, and phase models, to produce a predicted process variable, such as an end of stage or end of batch quality variable for use in analyzing the operation of the on-line process.

Non-intrusive data analytics in a process control system

Methods and apparatus to provide redundancy in a process control system are disclosed. An example method includes receiving, at a first network host, first integrity messages transmitted from a second network host over a first network. The method includes detecting a network failure over a first communication path between a first network interface of the first network host and a second network interface of the second network host via first network when a first network interface of the first network host fails to receive one of the first integrity messages expected from a first network interface of the second network host. The method includes automatically establishing a second communication path between the first network host and the second network host in response to the detected network failure.

Methods and apparatus to provide redundancy in a process control system

A process controller adaptation and tuning technique uses a closed loop adaptation cycle that performs an autocorrelation analysis on the prediction error or the control error of a process control system to determine if significant process model mismatch exists or to determine an increase or a decrease in process model mismatch over time. The adaptation and tuning technique may perform a controller tuning cycle when the determined model mismatch raises above a predetermined level.

Using autocorrelation to detect model mismatch in a process controller

Process control system and methods are disclosed. An example method includes operating a first cluster including first virtual machines and first servers and operating a second cluster including second virtual machines and second servers. The example method also includes storing first data from the first virtual machines at a first data store of the first cluster and storing a replica of the first data at a second data store of the second cluster. The example method also includes storing second data from the second virtual machines at the second data store and storing a replica of the second data at the first data store and identifying a failure of the first cluster. The method also includes, in response to the failure, restarting the first virtual machines using the second servers and the replica of the first data at the second data store.

Process control systems and methods

Example methods, apparatus and articles to test batch configurations are disclosed. A disclosed example method includes identifying, using a processor, an execution path through a batch configuration of a process control system, generating a test plan for the execution path, stimulating the process control system to execute the test plan, and recording a result of the test plan.

Methods, apparatus and articles of manufacture to test batch configurations

Example methods, apparatus and articles of manufacture to test process control systems are disclosed. A disclosed example method includes obtaining user inputs to a first process control system, obtaining process inputs and first process outputs of the first process control system, providing the user inputs and the process inputs to a second process control system to operate the second process control system, obtaining second process outputs of the second process control system operated with the user inputs and the process inputs, and comparing the first and second process outputs to determine whether the second process control system is implemented as intended.

Methods, apparatus and articles of manufacture to test process control systems

An MPC controller technique integrates feedback control performance better than methods commonly used today in MPC type controllers, resulting in an MPC controller that performs better than traditional MPC techniques in the presence of process model mismatch. In particular, MPC controller performance is enhanced by adding a tunable integration block to the MPC controller that develops an integral component indicative of the prediction or other control error, and adds this component to the output of an MPC controller algorithm to provide for faster or better control in the presence of model mismatch, which is the ultimate reason for the prediction error in the first place. This technique enables the MPC controller to react more quickly and to provide better set point change and load disturbance performance in the presence of model mismatch, without decreasing the robustness of the MPC controller.

Model predictive controller with tunable integral component to compensate for model mismatch

An MPC adaptation and tuning technique integrates feedback control performance better than methods commonly used today in MPC type controllers, resulting in an MPC adaptation/tuning technique that performs better than traditional MPC techniques in the presence of process model mismatch. The MPC controller performance is enhanced by adding a controller adaptation/tuning unit to an MPC controller, which adaptation/tuning unit implements an optimization routine to determine the best or most optimal set of controller design and/or tuning parameters to use within the MPC controller during on-line process control in the presence of a specific amount of model mismatch or a range of model mismatch. The adaptation/tuning unit determines one or more MPC controller tuning and design parameters, including for example, an MPC form, penalty factors for either or both of an MPC controller and an observer and a controller model for use in the MPC controller, based on a previously determined process model and either a known or an expected process model mismatch or process model mismatch range. A closed loop adaptation cycle may be implemented by performing an autocorrelation analysis on the prediction error or the control error to determine when significant process model mismatch exists or to determine an increase or a decrease in process model mismatch over time.

Robust adaptive model predictive controller with tuning to compensate for model mismatch

A method of creating and using an adaptive DMC type or other MPC controller includes using a model switching technique to periodically determine a process model, such as a parameterized process model, for a process loop on-line during operation of the process. The method then uses the process model to generate an MPC control model and creates and downloads an MPC controller algorithm to an MPC controller based on the new control model while the MPC controller is operating on-line. This technique, which is generally applicable to single-loop MPC controllers and is particularly useful in MPC controllers with a control horizon of one or two, enables an MPC controller to be adapted during the normal operation of the process, so as to change the process model on which the MPC controller is based to thereby account for process changes. The adaptive MPC controller is not computationally expensive and can therefore be easily implemented within a distributed controller of a process control system, while providing the same or in some cases better control than a PID controller, especially in dead time dominant process loops, and in process loops that are subject to process model mismatch within the process time to steady state.

On-line adaptive model predictive control in a process control system

A robust method of creating process models for use in controller generation, such as in MPC controller generation, adds noise to the process data collected and used in the model generation process. In particular, a robust method of creating a parametric process model first collects process outputs based on known test input signals or sequences, adds random noise to the collected process data and then uses a standard or known technique to determine a process model from the collected process data. Unlike existing techniques for noise removal that focus on clean up of non-random noise prior to generating a process model, the addition of random, zero-mean noise to the process data enables, in many cases, the generation of an acceptable parametric process model in situations where no process model parameter convergence was otherwise obtained. Additionally, process models created using this technique generally have wider confidence intervals, therefore providing a model that works adequately in many process situations without needing to manually or graphically change the model.

Robust process model identification in model based control techniques

An interface or display routine is provided for use in viewing and configuring a function block that performs integrated optimization and control within a process control system. The interface routine may enable a user to view or configure variables, values or other parameters associated with the integrated optimization and control block within the process control system. For example, the interface routine may display the current operating state of the integrated function block, may enable a user to select inputs and output of the function block for use in providing integrated optimization and control, may enable a user to select a particular or desired optimization function for use in the function block, etc. The interface routine may also display the multiple input output curves associated with the optimizer and the controller sections of the integrated function block in a manner that provides ease of view and selection of these curves as part of the algorithm used by the integrated function block.

Configuration and viewing display for an integrated model predictive control and optimizer function block

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.

Growing community of inventors

Austin, TX, United States of America

Dirk Thiele