Abstract: A process is modeled by resolving the process into a plurality of process stages, including at least a first process stage and a second process stage, and developing a plurality of models, each model corresponding to a respective one of the plurality of process stages, wherein the model corresponding to each process stage is developed using data from one or more runs of that process stage and output quality data relating to the one or more runs of that process stage and wherein the model corresponding to each process stage is adapted to produce an output quality prediction associated with that process stage, and wherein the output quality prediction produced by the model of a first one of the process stages is used to develop the model of a second one of the process stages.

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: Techniques for automatically determining, without user input, one or more sources of a variation in the behavior of a target process element operating to control a process in a process plant include using a process element alignment map to determine process elements upstream of the target process element in the process; performing a data analysis on data corresponding to the upstream elements with respect to the target element to determine behavior time offsets, strengths of impact, and impact delays; and determining the source(s) based on the data analysis outputs. Techniques may include automatically defining the process element alignment map by obtaining and processing data from a plurality of diagrams or data sources of the process and/or plant. Furthermore, the techniques may be performed during plant run-time by any high-volume, high density device such as centralized or embedded big data appliances, controllers, field or I/O devices, and/or by an unsupervised application.

Abstract: A process modeling technique uses a single statistical model developed from historical data for a typical process and uses this model to perform quality prediction or fault detection for various different process states of a process. The modeling technique determines means (and possibly standard deviations) of process parameters for each of a set of product grades, throughputs, etc., compares on-line process parameter measurements to these means and uses these comparisons in a single process model to perform quality prediction or fault detection across the various states of the process. In this manner, a single process model can be used to perform quality prediction or fault detection while the process is operating in any of the defined process stages or states.

Abstract: A system and method for creating and incorporating a function block within a process control system enables a user of the process control system to generate a function block by combining a plurality of files selected from a group of files provided by the manufacturer of the process control system to form a source code file associated with the function block. The user can modify the function block source code file to include a procedure, routine or algorithm that is not provided by the manufacturer and can send the modified source code file to the manufacturer for validation. If the function block source code file is validated, a security measure such as a digital signature is provided to the user that enables the user to incorporate the function block within the process control system.

Abstract: A control technique controls a process in a manner that reduces the number of controller changes provided to a controlled device, and so reduces the power consumption of the controlled device along with the loading of a process control communications network disposed between the controller and the controlled device. This technique is very useful in a control system having wirelessly connected field devices, such as sensors and valves which, in many cases, operate off of battery power. Moreover, the control technique is useful in implementing a control system in which control signals are subject to intermittent, non-synchronized or significantly delayed communications and/or in a control system that receives intermittent, non-synchronized or significantly delayed process variable measurements to be used as feedback signals in the performance of closed-loop control.

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: 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.

Abstract: 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.

Abstract: A distributed big data device in a process plant includes an embedded big data appliance configured to locally stream and store, as big data, data that is generated, received, or observed by the device, and to perform one or more learning analyses on at least a portion of the stored data. The embedded big data appliance generates or creates learned knowledge based on a result of the learning analysis, which the device may use to modify its operation to control a process in real-time in the process plant, and/or which the device may transmit to other devices in the process plant. The distributed big data device may be a field device, a controller, an input/output device, or other process plant device, and may utilize learned knowledge created by other devices when performing its learning analysis.

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 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 control technique that enables the use of slow or intermittently received process variable values in a predictor based control scheme without the need to change the control algorithm includes a controller, such as a PID controller, and a predictor, such as a model based predictor, coupled to receive intermittent feedback in the form of, for example, process variable measurement signals from a process. The predictor, which may be an observer like a Kalman filter, or which may be a Smith predictor, is configured to produce an estimate of the process variable value from the intermittent or slow process feedback signals while providing a new process variable estimate to the controller during each of the controller execution cycles to enable the controller to produce a control signal used to control the process.

Abstract: A control technique that enables the use of received process variable values in a Kalman filter based control scheme without the need to change the control algorithm includes a controller, such as a PID controller, and a Kalman filter, coupled to receive feedback in the form of, for example, process variable measurement signals from a process. The Kalman filter is configured to produce an estimate of the process variable value from slow or intermittent process feedback signals while providing a new process variable estimate to the controller during each of the controller execution cycles to enable the controller to produce a control signal used to control the process. The Kalman filter is also configured to compensate the process variable estimate for process noise with non-zero mean value that may be present in the process. The Kalman filter may apply this compensation to both continuously and intermittently received process variable values.

Abstract: 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.

Abstract: A mesh communication network for use in, for example, process control plants includes a plurality of network devices transmitting and receiving data according to a network schedule defined as a set of concurrent overlapping superframes, and along a set of graphs defining communication paths between pairs of network devices. A network manager residing in or outside the communication network develops a routing scheme for the network by analyzing the topology of the network and defining a set of graphs for use in routing or transmitting data between various nodes of the network, each graph including one or more communication paths between pairs of network devices. Concurrently or consequently, the network manager defines the network schedule in view of at least transmission requirements, power availability, and signal quality at each network device.

Abstract: In order to reduce or eliminate aliasing in a process control network, filtering of a measurement signal may be set based on the module execution rate in a process control system. A Nyquist frequency for the module may be determined based on the module execution rate where the Nyquist frequency may be twice the execution rate. Filtering after an analog to digital convertor may be set based on the module execution rate. In the analog to digital convertor, digital filtering after the converter may be set based on the module execution rate and the frequency content of the analog signal may be attenuated by a filter at and above the Nyquist frequency for the module execution rate.

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: 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.

Abstract: 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.