Abstract: A big data network or system for a process control system or plant includes a big data apparatus including a data storage area configured to store, using a common data schema, multiple types of process data and/or plant data (such as configuration and real-time data) that is used in, generated by or received by the process control system, and one or more data receiver computing devices to receive the data from multiple nodes or devices. The data may be cached and time-stamped at the nodes and streamed to the big data apparatus for storage. The process control system big data system provides services and/or data analyses to automatically or manually discover prescriptive and/or predictive knowledge, and to determine, based on the discovered knowledge, changes and/or additions to the process control system and to the set of services and/or analyses to optimize the process control system or plant.

Abstract: A device supporting big data in a process plant includes an interface to a communications network, a cache configured to store data observed by the device, and a multi-processing element processor to cause the data to be cached and transmitted (e.g., streamed) for historization at a unitary, logical centralized data storage area. The data storage area stores multiple types of process control or plant data using a common format. The device time-stamps the cached data, and, in some cases, all data that is generated or created by or received at the device may be cached and/or streamed. The device may be a field device, a controller, an input/output device, a network management device, a user interface device, or a historian device, and the device may be a node of a network supporting big data in the process plant. Multiple devices in the network may support layered or leveled caching of data.

Abstract: Smart process objects, which have both graphical and simulation elements, may be used to create one or more graphic displays and one or more process simulation modules, each having elements which may communicate with one another and with devices within a process plant to model and depict the operation of a process plant. The smart process objects may include one or more device objects, which represent physical devices within the process plant, and may include one or more smart connection objects which represent and model the flow of a material, such as a gas, a liquid, a composition of solid, electricity, etc., through a connection between entities within the process plant. The smart process objects may also include one or more smart stream objects, which also may represent and model the flow of a material at a particular point in the process plant.

Abstract: A data modeling studio provides a structured environment for graphically creating and executing models which may be configured for diagnosis, prognosis, analysis, identifying relationships, etc., within a process plant. The data modeling studio includes a configuration engine for generating user interface elements to facilitate graphical construction of a model and a runtime engine for executing data models in, for example, an offline or an on-line environment. The configuration engine includes an interface routine that generates user interface elements, a plurality of templates stored in memory that serve as the building blocks of the model and a model compiler that converts the graphical model into a data format executable by the run-time engine. The run time engine executes the model to produce the desired output and may include a retrieval routine for retrieving data corresponding to the templates from memory and a modeling routine for executing the executable model.

Abstract: A big data network or system for a process control system or plant includes a big data apparatus including a data storage area configured to store, using a common data schema, multiple types of process data and/or plant data (such as configuration and real-time data) that is used in, generated by or received by the process control system, and one or more data receiver computing devices to receive the data from multiple nodes or devices. The data may be cached and time-stamped at the nodes and streamed to the big data apparatus for storage. The process control system big data system provides services and/or data analyses to automatically or manually discover prescriptive and/or predictive knowledge, and to determine, based on the discovered knowledge, changes and/or additions to the process control system and to the set of services and/or analyses to optimize the process control system or plant.

Abstract: A big data network or system for a process control system or plant includes a big data apparatus including a data storage area configured to store, using a common data schema, multiple types of process data and/or plant data (such as configuration and real-time data) that is used in, generated by or received by the process control system, and one or more data receiver computing devices to receive the data from multiple nodes or devices. The data may be cached and time-stamped at the nodes and streamed to the big data apparatus for storage. The process control system big data system provides services and/or data analyzes to automatically or manually discover prescriptive and/or predictive knowledge, and to determine, based on the discovered knowledge, changes and/or additions to the process control system and to the set of services and/or analyzes to optimize the process control system or plant.

Abstract: A device supporting big data in a process plant includes an interface to a communications network, a cache configured to store data observed by the device, and a multi-processing element processor to cause the data to be cached and transmitted (e.g., streamed) for historization at a unitary, logical centralized data storage area. The data storage area stores multiple types of process control or plant data using a common format. The device time-stamps the cached data, and, in some cases, all data that is generated or created by or received at the device may be cached and/or streamed. The device may be a field device, a controller, an input/output device, a network management device, a user interface device, or a historian device, and the device may be a node of a network supporting big data in the process plant. Multiple devices in the network may support layered or leveled caching of data.

Abstract: A data modeling studio provides a structured environment for graphically creating and executing models which may be configured for diagnosis, prognosis, analysis, identifying relationships, etc., within a process plant. The data modeling studio includes a configuration engine for generating user interface elements to facilitate graphical construction of a model and a runtime engine for executing data models in, for example, an offline or an on-line environment. The configuration engine includes an interface routine that generates user interface elements, a plurality of templates stored in memory that serve as the building blocks of the model and a model compiler that converts the graphical model into a data format executable by the run-time engine. The run time engine executes the model to produce the desired output and may include a retrieval routine for retrieving data corresponding to the templates from memory and a modeling routine for executing the executable model.

Abstract: A big data network or system for a process control system or plant includes a big data apparatus including a data storage area configured to store, using a common data schema, multiple types of process data and/or plant data (such as configuration and real-time data) that is used in, generated by or received by the process control system, and one or more data receiver computing devices to receive the data from multiple nodes or devices. The data may be cached and time-stamped at the nodes and streamed to the big data apparatus for storage. The process control system big data system provides services and/or data analyses to automatically or manually discover prescriptive and/or predictive knowledge, and to determine, based on the discovered knowledge, changes and/or additions to the process control system and to the set of services and/or analyses to optimize the process control system or plant.

Abstract: A process control system includes economic models disposed in communication with process control modules, as well as with sources of economic data such as cost, throughput and profit data, and uses the economic models to determine useful economic parameters or information associated with the actual operation of the process plant at the time the plant is operating. The economic models can be used to provide financial statistics such as profitability, cost of manufactured product, etc. in real time based on the actual current operating state of the process and the business data associated with the finished product, raw materials, etc. These financial statistics can be used to drive alarms and alerts within the process network and be used as inputs to process plant optimizers, etc. to provide for better or more optimal control of the process and to provide a better understanding of the conditions which lead to maximum profitability of the plant.

Abstract: A process control system includes economic models disposed in communication with process control modules, as well as with sources of economic data, such as cost, throughput and profit data, and uses the economic models to determine useful economic parameters or information associated with the actual operation of the process plant at the time the plant is operating. The economic models can be used to provide financial statistics such as profitability, cost of manufactured product, etc. in real time based on the actual current operating state of the process and the business data associated with the finished product, raw materials, etc. These financial statistics can be used to drive alarms and alerts within the process network and be used as inputs to process plant optimizers, etc. to provide for better or more optimal control of the process and to provide a better understanding of the conditions which lead to maximum profitability of the plant.

Abstract: A process control system includes economic models disposed in communication with process control modules, as well as with sources of economic data, such as cost, throughput and profit data, and uses the economic models to determine useful economic parameters or information associated with the actual operation of the process plant at the time the plant is operating. The economic models can be used to provide financial statistics such as profitability, cost of manufactured product, etc. in real time based on the actual current operating state of the process and the business data associated with the finished product, raw materials, etc. These financial statistics can be used to drive alarms and alerts within the process network and be used as inputs to process plant optimizers, etc. to provide for better or more optimal control of the process and to provide a better understanding of the conditions which lead to maximum profitability of the plant.

Abstract: A process control configuration and management system provides a plurality of function blocks representing a plurality of devices in relation to a spatial layout of a facility in which the process control system is implemented. The configuration and management system also provides process control information and process simulation information related to each of the plurality of devices in relation to the spatial layout of the facility. The configuration and management system may be implemented on a handheld device and it may include a geographic positioning system providing geographic positioning data related to the handheld device and various devices in relation to the spatial layout of the facility.

Abstract: A simulation system attaches to a configuration database for one or more fully configured nodes of a process control system and copies the modules within those nodes into one or more simulation computers as simulation modules. The simulation system includes an algorithm that automatically creates a reference parameter module for each of the copied nodes that stores a reference parameter for each of the inputs, outputs or other references to signals external to the nodes. In the case of the Fieldbus function blocks, the simulation system automatically creates a function block within the simulation workstation in the protocol of the controller node which mimics the operation of the Fieldbus function block, and then creates the simulation system having those mimicked Fieldbus function blocks operating as if they had originally been configured to be in the controller being simulated, instead of within a field device external to the controller.

Abstract: A method and device or monitoring parameters in a process reduces power consumption and bandwidth requirements in the transmission of the monitored parameter to for example, a monitoring application. In particular, new monitoring parameter values are only transmitted if the difference between a measured or calculated parameter value and the most recently transmitted value exceeds a limit, since the last communication of the parameter or if the time since the last communication of the parameter exceeds a preset refresh period. The determination as to whether to transmit the parameter can be applied to engineering unit values or raw values.

Abstract: 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.

Abstract: A simulation system that includes interconnected simulation blocks which use process models to perform simulation activities for a process plant is integrated into a process control environment for the process plant in a manner that makes the simulation system easy to use and easily updated for on-line process simulation. The disclosed simulation system enables future predicted values as well as the current predicted values of process parameters produced by the simulation system to be made available for performance evaluation as well as to guide plant operations. Additionally, the simulation system is connected to the operating process plant to receive various on-line process plant measurements, and uses these measurements to automatically update the process models used in the simulation system, to thereby keep the simulation system coordinated with the actual operating conditions of the process plant.

Abstract: A process control configuration and management system provides a plurality of function blocks representing a plurality of devices in relation to a spatial layout of a facility in which the process control system is implemented. The configuration and management system also provides process control information and process simulation information related to each of the plurality of devices in relation to the spatial layout of the facility. The configuration and management system may be implemented on a handheld device and it may include a geographic positioning system providing geographic positioning data related to the handheld device and various devices in relation to the spatial layout of the facility.

Abstract: A process control system element for use in a process plant includes a process module, which represents a logical unit in the process plant, integrated with an expert module capable of detecting and/or mitigating abnormal situations associated with the logical unit. The process module may include a plurality of process objects, each process object representing a corresponding physical entity in the process plant. At least some of the process objects may include simulation capabilities to simulate the corresponding physical entities. The expert module may be adapted to detect at least one abnormal situation associated with the logical unit, and may be communicatively coupled to the process module to receive data from the process module during operation of the process module. Because the expert module is integrated with the process module, a user can more easily configure the expert module to detect and/or mitigate abnormal situations associated with the logical unit.

Abstract: A system for facilitating configuration of a process plant may include a process graphics editor and a process module editor. The process graphics editor may facilitate creation and/or modification of a graphical representation of physical entities In the process plant. The process module editor may facilitate creation and/or modification of a process module. A process module may include one or more interconnected process objects representative of one or more corresponding physical entities in the process plant. The system may also include a supervisor module communicatively coupled to the process graphics editor and the process module editor. The supervisor module may be capable of detecting changes made to the graphical representation of the physical entities using the process graphics editor. In response to detecting such changes, the supervisor module may instruct the process module editor to make a corresponding change, if any, to a process module.