Abstract: Embodiments of the present disclosure provide for generation and use of multiple representations of a processing plant, for example a refinery plant. In some embodiments a first representation is utilized to visualize the component(s) and/or connection(s) of the processing plant, with a second representation utilized to process one or more aspect(s) of the processing plant. The first representation in some embodiments represents the physical components and/or connections between such components to enable intuitive visualization and monitoring of the components thereof. The second representation in some embodiments represents a virtualized representation of the physical components and/or connections between such components in a manner that is better processable via one or more computing process(es). Such processing is performable to generate data for controlling configuration and/or operation of component(s) in the processing plant.

Abstract: A field device, method, and non-transitory computer readable medium provide for cost of lost opportunity to operators in real-time using an advance process control. The field device includes a memory and a processor operably connected to the memory. The processor receives current values and average values for controlled variables and manipulated variables; determines costs of lost opportunity for each of controlled variable variance issues, limit issues, model quality issues, inferential quality issues, and variable model issues based on the current values and the average values of the controlled variables; and stores the costs of lost opportunity for the field device.

Abstract: A cascaded MPC system includes an upper tier controller and lower tier controller having stored constraints for controlling a process having manipulated variables (MVs), controlled variables (CVs), and conjoint manipulated variable (CMV). The upper tier passes a target value for the CMV to the lower tier which optimizes for determining a local optimal operating point for the MVs, CVs, and CMV, moves towards the target value starting at the CMVs local operating point, and optimizes for identifying of the constraints as selected constraint(s) when the moving is truncated, passes the selected constraint(s) to the upper tier which performs an overall optimization for the process using the selected constraint to generate an optimal value for the CMV that lower tier uses as a new CM V target value for redetermining updated local optimal operating points for the MVs, CVs, and CMV, and for controlling the process utilizing the updated operating points.

Abstract: A method of generating a hybrid model predictive control (MPC) simulation model for a plant configured to run a process that processes at least one raw material to generate at least one tangible product. A predictive dynamic MPC sub-model is provided for each of plurality of process units in the plant, the plant including at least one process controller coupled to field devices coupled to the plurality of process units, where the process units comprise equipment for converting the raw material or an intermediate material formed from the raw material into to another material. A piping network diagram is obtained that provides a representation of a piping network for routing of the raw material and the intermediate material during the process. The dynamic MPC sub-models are coupled together using the piping network to generate the hybrid MPC simulation model which models the plant as a whole.

Abstract: 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.

Abstract: 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.

Abstract: A cascaded MPC system includes an upper tier controller and lower tier controller having stored constraints for controlling a process having manipulated variables (MVs), controlled variables (CVs), and conjoint manipulated variable (CMV). The upper tier passes a target value for the CMV to the lower tier which optimizes for determining a local optimal operating point for the MVs, CVs, and CMV, moves towards the target value starting at the CMVs local operating point, and optimizes for identifying of the constraints as selected constraint(s) when the moving is truncated, passes the selected constraint(s) to the upper tier which performs an overall optimization for the process using the selected constraint to generate an optimal value for the CMV that lower tier uses as a new CMV target value for redetermining updated local optimal operating points for the MVs, CVs, and CMV, and for controlling the process utilizing the updated operating points.

Abstract: A method of generating a hybrid model predictive control (MPC) simulation model for a plant configured to run a process that processes at least one raw material to generate at least one tangible product. A predictive dynamic MPC sub-model is provided for each of plurality of process units in the plant, the plant including at least one process controller coupled to field devices coupled to the plurality of process units, where the process units comprise equipment for converting the raw material or an intermediate material formed from the raw material into to another material. A piping network diagram is obtained that provides a representation of a piping network for routing of the raw material and the intermediate material during the process. The dynamic MPC sub-models are coupled together using the piping network to generate the hybrid MPC simulation model which models the plant as a whole.

Abstract: A field device, method, and non-transitory computer readable medium provide for cost of lost opportunity to operators in real-time using an advance process control. The field device includes a memory and a processor operably connected to the memory. The processor receives current values and average values for controlled variables and manipulated variables; determines costs of lost opportunity for each of controlled variable variance issues, limit issues, model quality issues, inferential quality issues, and variable model issues based on the current values and the average values of the controlled variables; and stores the costs of lost opportunity for the field device.

Abstract: 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.

Abstract: A method includes identifying one or more proxy limit variables to be used to define an operating envelope associated with an industrial process controller. The industrial process controller is associated with a number of process variables including the proxy limit variable(s). The method also includes maximizing and minimizing at least one of the proxy limit variable(s). The method further includes presenting maximum and minimum values of the at least one of the proxy limit variable(s) as the operating envelope in a graphical user interface to at least one user. A single proxy limit variable could be used, and maximum and minimum values of the single proxy limit variable can be presented in a one-dimensional representation of the operating envelope. Multiple proxy limit variables could also be used, and maximum and minimum values of the proxy limit variables can be presented in a multi-dimensional representation of the operating envelope.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method and device to insure that a particular dispensing package (18) can only be engaged into an appropriate dispensing location (14). Specifically, the package and the dispenser have mating concentric rings (24, 30) (or portions thereof) to prevent engagement and dispensing of an inappropriate product. In one embodiment, the package (18) is provided with one or more concentric ring projections (24) (or portions thereof) and the dispenser (10) is provided with one or more matching concentric ring recesses (30). The projections and the recesses have similar diameters to define a mating set. In some embodiments, the concentric ring projections are placed on a closure (20), such as a cap, of the package. In alternative embodiments, the projections are placed on the dispenser and the recesses are placed on the package.

Abstract: A method includes identifying one or more proxy limit variables to be used to define an operating envelope associated with an industrial process controller. The industrial process controller is associated with a number of process variables including the proxy limit variable(s). The method also includes maximizing and minimizing at least one of the proxy limit variable(s). The method further includes presenting maximum and minimum values of the at least one of the proxy limit variable(s) as the operating envelope in a graphical user interface to at least one user. A single proxy limit variable could be used, and maximum and minimum values of the single proxy limit variable can be presented in a one-dimensional representation of the operating envelope. Multiple proxy limit variables could also be used, and maximum and minimum values of the proxy limit variables can be presented in a multi-dimensional representation of the operating envelope.

Abstract: 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.

Abstract: 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.