Fisher-Rosemount Systems, Inc

Hart Communication Foundation

Fieldcomm Group, Inc.

Rosemount Inc.

Dust Networks, Inc.

Mark J Nixon

Terrence L Blevins

Anthony Amaro, Jr

Ken J Beoughter

Gary Keith Law

John Mark Caldwell

Eric Darrell Rotvold

Daniel Dean Christensen

Wilhelm K Wojsznis

Noel Howard Bell

Paul Richard Muston

Larry Oscar Jundt

Dennis L Stevenson

Robert Burke Havekost

Stephen Copps Gilbert

J Michael Lucas

Arthur Webb

Daniel R Strinden

Michael George Ott

Wallace A Pratt, Jr

Trevor Duncan Schleiss

Tomas Lennvall

Robin S Pramanik

Deji Chen

Peter Hartmann

Ling Zhou

Claudio Fayad

Mariana C Dionisio

Kent Allan Burr

David R Denison

Neil J Peterson

John Michael Lucas

Cindy Alsup Scott

Alper Turhan Enver

Julian Kevin Naidoo

Kim Ordean Van Camp

Marion A Keyes, Iv

Herschel Odis Koska, Ii

Robert G Halgren, Iii

Hoa Van Lai

Mike Lucas

Jian Li

Bruce Hubert Campney

Joshua Brian Kidd

Michael J Lucas

Ram Ramachandran

Cristopher Ian Sarmiento Uy

Christopher J Worek

Yuri Zats

Ron Eddie

Stephen Gerard Hammack

Gregory K McMillan

Tom Aneweer

Teresa A Chatkoff

Harvey S Smallman

Richard Clarence Dayo Fabros

Godfrey Roland Sherriff

Peter Wojsznis

Grant Wilson

John R Shepard

James Henry Moore, Jr

James Randall Balentine

Doyle Eugene Broom

Peter Zornio

Ryan Gallardo Valderama

Tennyson Hao

Matthew William Poplawski

Maia B Cook

Robert J Karschnia

Andrew Philip Dove

David L Deitz

Lee Allen Neitzel

Prashant Joshi

Duncan Schleiss

Camilo Fadul

Ebtesam S Tanyous

Terry Blevins

Frederick R Enns

Dirk Thiele

Brandon Hieb

Thomas L Phinney

Dan Dean Christensen

Christopher John Felts

Vasiliki Tzovla

Shawn William Anderson

Kurtis K Jensen

Mitchell Stephen Panther

Sergio Diaz

Jon Dale Westbrock

Aaron C Jones

Kenneth D Krivoshein

Ashish Mehta

Keith Richard Bellville

Tiong P Ong

Riyaz M Ali

Molly Marie Firkins

Andrew E Cutchin

Michael D Tajmajer

Adrian A Sanchez

Hubbard F Fellows

A model predictive control (MPC) device includes an input interface configured to receive an industrial process input associated with at least one component of a process automation plant, an output interface configured to transmit a control instruction to control the component, memory configured to store first and second MPC process models corresponding to different states, and a processor configured to identify a current state parameter of an industrial process, and predict a future industrial process output using the first or second MPC process model, based on the current state parameter being associated with the first or second MPC process model. The processor is configured to calculate a target operating point according to the predicted future industrial process output, determine a control signal to drive the industrial process to the calculated target operating point, and output the determined control signal to control operation of the component of the industrial process plant.

Model predictive control systems for process automation plants

An enterprise engineering and configuration system includes a common configuration database and support services stored in and executed in a compute fabric of an enterprise. The configuration database and support services use and implement a common configuration data schema to support the configuration of hardware and software in the compute fabric and at multiple different sites or physical locations of the enterprise even when different control and automation systems are used at these different sites or physical locations. The configuration system enables implementing hardware or software configuration changes to various different sites or locations of an enterprise either centrally from a configuration device connected directly to the compute fabric of the enterprise or locally from any physical location or site of the enterprise, while maintaining a single integrated enterprise configuration database that stores configuration data for each of the multiple sites of the enterprise. This configuration system is flexible as it enables engineering and configuration changes to be made by users anywhere in the enterprise for any of the sites of the enterprise and across different sites of the enterprise.

Enterprise engineering and configuration framework for advanced process control and monitoring systems

A Multi-Purpose Dynamic Simulation and run-time Control platform includes a virtual process environment coupled to a physical process environment, where components/nodes of the virtual and physical process environments cooperate to dynamically perform run-time process control of an industrial process plant and/or simulations thereof. Virtual components may include virtual run-time nodes and/or simulated nodes. The MPDSC includes an I/O Switch which delivers I/O data between virtual and/or physical nodes, e.g., by using publish/subscribe mechanisms, thereby virtualizing physical I/O process data delivery. Nodes serviced by the I/O Switch may include respective component behavior modules that are unaware as to whether or not they are being utilized on a virtual or physical node. Simulations may be performed in real-time and even in conjunction with run-time operations of the plant, and/or simulations may be manipulated as desired (speed, values, administration, etc.). The platform simultaneously supports simulation and run-time operations and interactions/intersections therebetween.

Automatic load balancing and performance leveling of virtual nodes running real-time control in process control systems

An architecture supporting a process control or automation system may include an authentication service which determines whether an entity (e.g., a human, automated, virtual, or physical entity) is the party that/who the entity claims to be, and an authorization service which determines whether a request of the entity to access a resource is allowed or denied. The authentication service provides unique identities of entities and respective security credentials, which may include tokens utilized during authorization. The authorization service authorizes an entity to access a requested resource based on role-based permissions of a role to which the entity is assigned and resource access permissions protecting the requested resource. The role-based permissions and/or the resource access permissions may be respectively scoped to limit or restrict actions, activities, operations, and/or resource access based on specified criteria. Each entity may be authenticated, and each request of an authenticated entity may be respectively authorized.

Authentication/authorization framework for a process control or automation system

A software defined (SD) process control system (SDCS) includes a control container having contents which are executable during run-time of the process plant to control at least a portion of an industrial process. The SDCS also includes a security service associated with the control container and including contents which define one or more security conditions. The security service executes via a container on a compute node of the SDCS to control access to and/or data flow from the control container based on the contents of the security container.

Security services in a software defined control system

In one aspect, a micro-service control architecture provides a modular, flexible platform for designing, diagnosing, updating and/or expanding process control systems. Each service is containerized to provide portability and isolation from other components of the process control system. In another aspect, a function block diagram includes a 'shadow' block that acts as an interface to an external, custom calculation engine, thereby enabling the custom calculation engine to operate synchronously with respect to other function blocks of the function block diagram.

Modular process control system

A software defined distributed control system (SDCS) in a process plant includes an application layer that includes a plurality of containers instantiated in a data center cluster. Each of the containers is an isolated execution environment executing within the local operating system of a respective computing node. The containers cooperate to facilitate execution of a control strategy in the SDCS, and includes a hyper converged infrastructure (HCI) operating across the data center cluster, which HCI is configured to communicate with the application layer via an adapter service. The HCI includes software-defined (SD) compute resources, SD storage resources, SD networking resources, and an orchestrator service. The orchestrator service is programmed to configure a first container to include a service executing within the first container. It also assigns the first container to execute on an available hardware resource to control a plurality of field devices operating in the process plant.

Systems and methods for dynamically maintained redundancy and load balancing in software defined control systems for industrial process plants

A digital safety lock of a field device or other process plant equipment activates in response to receiving a request from a locking party, thereby placing the device into a locked mode. While the device is in the locked mode, only the locking party may perform maintenance activities on, or otherwise functionally control, the device. While locked, the device may provide an indication of the locking party to any other device or application attempting to access or control the locked device. Other devices, applications, and users may communicate with the locking party or locking party device to request that the device be unlocked and/or to request the corresponding digital safety lock key. Upon the device receiving the correct digital safety lock key, the digital safety lock may be deactivated, and the device may enter into an unlocked state if no other digital safety locks remain activated for the device.

Digital safety locks in industrial process plants

Industrial control system architecture for real-time simulation and process control

Techniques for detecting suspicious performance of a throttling control valve (also referred to herein as a 'valve') in a process plant are described herein. For each of N time periods, a computing device determines and analyzes process parameter values for process parameters related to a valve to determine a status of the valve for the time period. The computing device compares the valve statuses over the N time periods to determine whether the valve is operating well for at least a threshold portion of at least a subset of the N time periods. In response to determining that the valve is not operating well for at least the threshold portion of at least the subset of the N time periods, the computing device determines that the valve is suspected of performing poorly, and provides an indication of the suspect valve to a user interface for display to a user.

Suspicious control valve performance detection

Techniques for configuring layouts and display view hierarchies and presenting display views in an operating environment of a process plant according to the layouts and display view hierarchies include providing graphical user controls at a graphical display configuration application for defining a layout. The layouts indicate a manner in which divide one or several display screens into one or several display regions, where each display region presents a display view that includes indications of process control elements. The graphical display configuration application also includes graphical user controls for defining a display view hierarchy of the display views by assigning levels of detail to the display views and designating some of the display views as subviews of other display views. An operator application obtains the defined layout and display view hierarchy and presents the display views at an operator workstation according to the obtained layout and/or display view hierarchy.

Systems and methods for configuring and presenting a display navigation hierarchy in a process plant

Process control systems for operating process plants are disclosed herein. The process control systems include control modules that are decoupled from the I/O architecture of the process plants using signal objects or generic shadow blocks. This decoupling is effected by using the signal objects or generic shadow blocks to manage at least part of the communication between the control modules and the field devices. Signal objects may convert between protocols used by control modules and field devices, thus decoupling the control modules from the I/O architecture. Generic shadow blocks may be automatically configured to mimic the operation of field devices within a controller executing the control modules, thus partially decoupling the control modules from the I/O architecture by using the shadow blocks to manage communication between the control modules and the field devices.

Architecture-independent process control

To provide a trusted, secure, and immutable record of storage operations executed by a storage center for storing measurement data provided by a process plant, techniques are described for utilizing a distributed ledger. When a data contributor such as a process plant generates measurement data, an encrypted version of a set of measurement data is transmitted to a storage center for secure storage of the measurement data. In some instances, the data contributor divides the set of measurement data into several subsets and transmits each subset of encrypted measurement data to a different storage center. Furthermore, the storage center generates a transaction for the storage operation which is recorded in a distributed ledger. When a data subscriber retrieves the encrypted measurement data from a storage center, the data subscriber can verify the authenticity of the data based on the information recorded in the distributed ledger.

Framework for privacy-preserving big-data sharing using distributed ledger

Publish/subscribe protocol for real-time process control

A process control system includes a plurality of field devices operating to control a process in a process plant. A communication infrastructure couples the plurality of field devices to a software-defined control system (SDCS) that receives data from the field devices and transmits instructions to the field devices. A data cluster, executing the SDCS, includes a plurality of compute nodes, each of which includes a processor executing an operating system, a memory, and a communication resource coupled to one or more other compute nodes in the data cluster. A plurality of instantiated containers, each of which is an isolated execution environment within the operating system of the compute node on which the container is instantiated, cooperate to facilitate execution of a control strategy in the SDCS. At least one of the containers in the SDCS is pinned to a component in the SDCS.

Systems and methods for associating modules in a software defined control system for industrial process plants

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Round Rock, TX, United States of America

Mark J Nixon