A configuration tool includes a tangible, non-transitory computer-readable medium having computer-executable instructions for configuring a model of a technical system and displaying the model on a display connected to a computer. The model includes at least two model components. Each model component has at least one port. Each model component is displayable in an expanded component representation on the display. The at least one port of each model component is connectable to at least one port of another model component by port association lines. Each model component is displayable in an expanded line representation on the display along with the at least one port and the port association lines of each model component. At least for one selected model component the port association lines connected to ports of the selected model component can be selected to be displayed in a reduced line representation.

A method includes obtaining data identifying values of one or more controlled variables associated with an industrial process controller. The method also includes identifying periods when at least one of the one or more controlled variables has been moved to an associated limit by the controller. The method further includes, for each identified period, (i) identifying a standard deviation of predicted values for the associated controlled variable and (ii) determining a control giveaway value for the associated controlled variable based on the standard deviation. The control giveaway value is associated with an offset between the associated controlled variable's average value and the associated limit. In addition, the method includes identifying variances in the one or more controlled variables using the control giveaway values and generating a graphical display identifying one or more impacts or causes for at least some of the variances.

A method includes obtaining data associated with operation of an industrial process controller and identifying impacts of operational problems of the industrial process controller. The method also includes generating a graphical display for a user, where the graphical display presents one or more recommended actions to reduce or eliminate at least one of the impacts of at least one of the operational problems. The method further includes triggering at least one of the one or more recommended actions based on input from the user. The method could also include executing one or more analytic algorithms to process the obtained data and identify the operational problems of the industrial process controller. Each of the one or more analytic algorithms could be instantiated as a container, and multiple containers could be instantiated and executed as needed. Results of executing the one or more analytic algorithms could be transformed into a standard format.

A method includes obtaining data associated with operation of a model-based industrial process controller. The method also includes identifying at least one estimated impact of at least one operational problem of the industrial process controller, where each estimated impact is expressed in terms of a lost opportunity associated with operation of the industrial process controller. The method further includes presenting the at least one estimated impact to a user. The at least one estimated impact could include impacts associated with noise or variance in process variables used by the industrial process controller, misconfiguration of an optimizer in the industrial process controller, one or more limits on one or more process variables, a quality of at least one model used by the industrial process controller, a quality of one or more inferred properties used by the industrial process controller, or one or more process variables being dropped from use by the industrial process controller.

A method includes obtaining data identifying values of one or more process variables associated with an industrial process controller and identifying one or more constraint violations using the data. The method also includes, for each identified constraint violation, analyzing a behavior of the controller, a behavior of an industrial process being controlled, and how the controller was being used by at least one operator for a period of time. At least part of the period of time is prior to the identified constraint violation. The method further includes generating a graphical display based on the analysis, where the graphical display identifies one or more probable causes for at least one of the one or more constraint violations.

A multiple-input/multiple-output control routine in the form of a model predictive control (MPC) routine operates with wireless or other sensors that provide non-periodic, intermittent or otherwise delayed process variable measurement signals at an effective rate that is slower than the MPC controller scan or execution rate. The wireless MPC routine operates normally even when the measurement scan period for the controlled process variables is significantly larger than the operational scan period of the MPC controller routine, while providing control signals that enable control of the process in a robust and acceptable manner. During operation, the MPC routine uses an internal process model to simulate one or more measured process parameter values without performing model bias correction during the scan periods at which no new process parameter measurements are transmitted to the controller. When a new measurement for a particular process variable is available at the controller, the model prediction and simulated parameter values are updated with model bias correction based on the new measurement value, according to traditional MPC techniques.

A system includes an apparatus and a processor. The apparatus includes a set of actuator elements that move between two positions. Each actuator element is comprised in: exactly one first subset out of a plurality of non-empty first subsets and exactly one second subset out of a plurality of non-empty second subsets. The processor is configured to generate one or more control commands for a group of subsets out of the first and the second pluralities of subsets in response to a number of moving elements which, if released from the first extreme position during a second sampling cycle, enables production by the apparatus during the second sampling cycle of a sound.

A system includes an apparatus and a processor. The apparatus includes a set of actuator elements that move between two positions. Each actuator element is comprised in: exactly one first subset out of a plurality of non-empty first subsets and exactly one second subset out of a plurality of non-empty second subsets. The processor is configured to generate one or more control commands for a group of subsets out of the first and the second pluralities of subsets in response to a number of moving elements which, if released from the first extreme position during a second sampling cycle, enables production by the apparatus during the second sampling cycle of a sound.

A computer-implemented method for generating a control program that is executable on a control system from a graphical control model. A better utilization of the control system is achieved in that the graphical control model is translated into program code such that the generated program code has at least one FXP operation and at least one FLP operation, and in that the generated program code is translated into the executable control program such that when the control program is executed on the control system a portion of the control program is executed on the FXP unit and another portion of the control program is executed on the FLP unit.

According to one embodiment, a system for digital process management comprises at least one memory and processor communicatively coupled to one another. The memory is operable to store input data, output data, and a process model, each associated with a digital process management system. The processor is operable to determine the process model, which comprises a set of rules, determine the input data based upon data received from a data source, and determine whether to initiate the process model based upon the input data. The system is further operable to execute the process model across two of an Internet-connected device, a gateway, and an enterprise by determining a state of a process, determining an activity based upon the state and the set of rules, executing the activity, determining results data, and determining the output data based upon the results data. The system is further operable to communicate the output data.