Fisher-rosemount Systems, Inc

Noel Howard Bell

Mark J Nixon

Gary Keith Law

Anthony Amaro, Jr

John Mark Caldwell

Paul Richard Muston

Alper Turhan Enver

Joshua Brian Kidd

Dirk Thiele

Tom Aneweer

Harvey S Smallman

Brian Crandall

Cory A Rieth

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.

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

Publish/subscribe protocol for real-time process control

Virtualized real-time I/O in process control systems

Centralized virtualization management node in process control systems

Ease of node switchovers in process control systems

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

Distributed industrial process monitoring and analytics systems and methods are provided for operation within a process plant. A plurality of distributed data engines (DDEs) may be embedded within the process plant to collect and store data generated by data sources, such as process controllers. Thus, the data may be stored in a distributed manner in the DDEs embedded throughout the process plant. The DDEs may be connected by a data analytics network to facilitate data transmission by subscription or query. The DDEs may be configured as a plurality of clusters, which may further include local and centralized clusters. The local clusters may obtain streaming data from data sources and stream selected data to a data consumer. The centralized cluster may register the local clusters, receive data therefrom, and perform data analytic functions on the received data. The analyzed data may be further sent to a data consumer.

Distributed industrial performance monitoring and analytics

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Austin, TX, United States of America

Noel Howard Bell