Abstract: A computer-implemented method for using a standalone tool for certificate management is provided. The standalone tool for certificate management is provided between a plurality of computing nodes and a management node. The standalone tool determines a certificate status for each of the plurality of computing nodes in the computing system. The standalone tool also determines any certificate operations for each of the plurality of computing nodes in the computing system. The certificate status and any of the certificate operations are presented in a consolidated view.

Abstract: Methods and apparatus are disclosed that deploy a hybrid workload domain. An example apparatus includes a resource discoverer to determine whether a first bare metal server is available and a resource allocator to allocate virtual servers for a virtual server pool based on an availability of the virtual servers and, when the first bare metal server is available, allocate the first bare metal server for a bare metal server pool. The example apparatus further includes a hybrid workload domain generator to generate, for display in a user interface, a combination of die virtual server pool and the bare metal server pool and generate a hybrid workload domain used to run a user application based on a user selection in a user interface, the hybrid workload domain including virtual servers from the virtual server pool and bare metal servers from the bare metal server pool.

Abstract: A method implemented using a server includes receiving, from a first application, a tag associated with an equipment, the tag indicating an event. The method also includes retrieving, from at least a second application, at least one of an action or context information linked to the event based on the tag. The method further includes transmitting the tag indicating the event and the action linked to event. In addition, the method includes displaying, on a plant graphic, a location of the event using the tag and the action linked to the event.

Abstract: Heating, ventilation, and air conditioning (HVAC) system controllers are described herein. One HVAC controller includes a user interface configured to receive an indication of whether there is a positive or negative relationship between each of a number of controlled variables of an HVAC system and each of a number of manipulated variables of the HVAC system, a memory, and a processor configured to execute executable instructions stored in the memory to determine operating parameters for each of the number of manipulated variables, wherein the determined operating parameters are determined based, at least in part, on whether there is a positive or negative relationship between each respective controlled variable and each respective manipulated variable, and the determined operating parameters are not based on system dynamics and disturbances associated with the HVAC system. The determined operating parameters may be optimal with respect to the current condition of the HVAC system.

Abstract: Heating, ventilation, and air conditioning (HVAC) controllers are described herein. One method includes receiving an approximate relationship between each of a number of controlled and manipulated variables of an HVAC system, designating one of the number of controlled variables as a primary controlled variable, determining operating parameters for each of the number of manipulated variables that maintain the primary controlled variable based, at least in part, on the approximate relationship between the primary controlled variable and each respective manipulated variable, and determining operating parameters for each of the number of manipulated variables that maintain each of the other controlled variables based, at least in part, on the approximate relationship between each respective other controlled variable and each respective manipulated variable and the determined operating parameters for each of the number of manipulated variables that maintain the primary controlled variable.

Abstract: Heating, ventilation, and air conditioning (HVAC) controllers are described herein. One method includes receiving an approximate relationship between each of a number of controlled and manipulated variables of an HVAC system, designating one of the number of controlled variables as a primary controlled variable, determining operating parameters for each of the number of manipulated variables that maintain the primary controlled variable based, at least in part, on the approximate relationship between the primary controlled variable and each respective manipulated variable, and determining operating parameters for each of the number of manipulated variables that maintain each of the other controlled variables based, at least in part, on the approximate relationship between each respective other controlled variable and each respective manipulated variable and the determined operating parameters for each of the number of manipulated variables that maintain the primary controlled variable.

Abstract: A controller and loop performance monitoring system is coupled to a controller, detects loop performance degradation in time, and diagnoses a cause of the loop performance degradation. If the cause of loop performance degradation is poor controller tuning, a re-tuning mechanism is triggered. If the cause of loop performance degradation is external to the controller (a disturbance acting on the loop, hardware malfunction etc.), an action defined in control strategy is taken, or the user is informed via alarm, user interface, or upper layer software that collects the performance measures. The monitoring itself is designed to be recursive and with low memory demands, so it can be implemented directly in the controller, without need for data transfer and storage. The monitoring is modular, consisting of oscillation detection and diagnosis part, performance indices part, internal logic part, and triggering part, easily extensible by other performance indices or parts (e.g. for overshoot monitoring).

Abstract: A method includes receiving an adjustment to a computational speed of a processing device associated with a model predictive controller (MPC) in an embedded execution platform of an industrial process control system. The method also includes receiving an adjustment to a memory footprint required for calculations performed by the processing device during operation of the MPC. The method further includes loading the MPC in the embedded platform, where the loading includes the adjustments to the computational speed and the memory footprint.

Abstract: A method includes, using at least one processing device, obtaining multiple diagnostic indicators associated with at least a portion of an industrial process system and combining the diagnostic indicators to form a generalized indicator. Each diagnostic indicator has a value, and the generalized indicator is associated with a position on a continuous scale. The continuous scale could include a color gradient, and the method could include displaying the generalized indicator along the color gradient with a color based on its position. Multiple generalized indicators associated with multiple portions of the process system could be displayed within a torus or circle, and different portions of the torus or circle can be associated with different portions of the process system. Different concentric tori or circles could be associated with different periods of time, and at least one concentric torus or circle could identify a predicted behavior of the process system.

Abstract: A method implemented using a server includes receiving, from a first application, a tag associated with an equipment, the tag indicating an event. The method also includes retrieving, from at least a second application, at least one of an action or context information linked to the event based on the tag. The method further includes transmitting the tag indicating the event and the action linked to event. In addition, the method includes displaying, on a plant graphic, a location of the event using the tag and the action linked to the event.

Abstract: A method includes performing an initial analysis using first data collected at a first collection frequency from a data source of a processing facility. The method also includes identifying at least one control loop in the processing facility having a potential problem based on the initial analysis and selecting a root cause analysis to execute in order to determine a root cause of the potential problem. The method further includes generating a schedule file and a loop identifier file associated with a request to collect, from the data source, second data for the at least one control loop at a higher second collection frequency for a specified duration. The request specifies the specified duration, the second collection frequency, and the at least one specified control loop. In addition, the method includes transmitting the schedule file to a data collector and the loop identifier file to a database.

Abstract: Heating, ventilation, and air conditioning (HVAC) system controllers are described herein. One HVAC controller includes a user interface configured to receive an indication of whether there is a positive or negative relationship between each of a number of controlled variables of an HVAC system and each of a number of manipulated variables of the HVAC system, a memory, and a processor configured to execute executable instructions stored in the memory to determine operating parameters for each of the number of manipulated variables, wherein the determined operating parameters are determined based, at least in part, on whether there is a positive or negative relationship between each respective controlled variable and each respective manipulated variable, and the determined operating parameters are not based on system dynamics and disturbances associated with the HVAC system. The determined operating parameters may be optimal with respect to the current condition of the HVAC system.

Abstract: Heating, ventilation, and air conditioning (HVAC) controllers are described herein. One method includes receiving an approximate relationship between each of a number of controlled and manipulated variables of an HVAC system, designating one of the number of controlled variables as a primary controlled variable, determining operating parameters for each of the number of manipulated variables that maintain the primary controlled variable based, at least in part, on the approximate relationship between the primary controlled variable and each respective manipulated variable, and determining operating parameters for each of the number of manipulated variables that maintain each of the other controlled variables based, at least in part, on the approximate relationship between each respective other controlled variable and each respective manipulated variable and the determined operating parameters for each of the number of manipulated variables that maintain the primary controlled variable.

Abstract: A method includes, using at least one processing device, obtaining multiple diagnostic indicators associated with at least a portion of an industrial process system and combining the diagnostic indicators to form a generalized indicator. Each diagnostic indicator has a value, and the generalized indicator is associated with a position on a continuous scale. The continuous scale could include a color gradient, and the method could include displaying the generalized indicator along the color gradient with a color based on its position. Multiple generalized indicators associated with multiple portions of the process system could be displayed within a torus or circle, and different portions of the torus or circle can be associated with different portions of the process system. Different concentric tori or circles could be associated with different periods of time, and at least one concentric torus or circle could identify a predicted behavior of the process system.

Abstract: A method of dynamic model selection for hybrid linear/non-linear process control includes developing a plurality of process models including at least one linear process model and at least one non-linear process model from inputs including dynamic process data from a processing system that runs a physical process. At least two of the plurality of process models are selected based on a performance comparison based on at least one metric, wherein the selected process models number less than a number of the plurality of process models received. A multi-model controller is generated that includes the selected process models. The physical process is simulated using the multi-model controller by applying the selected process models to obtain closed loop performance test data for each of the selected models. The performance test data is compared. A selected process model is then selected.

Abstract: A controller and loop performance monitoring system is coupled to a controller, detects loop performance degradation in time, and diagnoses a cause of the loop performance degradation. If the cause of loop performance degradation is poor controller tuning, a re-tuning mechanism is triggered. If the cause of loop performance degradation is external to the controller (a disturbance acting on the loop, hardware malfunction etc.), an action defined in control strategy is taken, or the user is informed via alarm, user interface, or upper layer software that collects the performance measures. The monitoring itself is designed to be recursive and with low memory demands, so it can be implemented directly in the controller, without need for data transfer and storage. The monitoring is modular, consisting of oscillation detection and diagnosis part, performance indices part, internal logic part, and triggering part, easily extensible by other performance indices or parts (e.g. for overshoot monitoring).

Abstract: A method for controlling physical processes includes providing a process model for a physical process run by a processing plant, where the process model represents an interaction between a plurality of controlled process variables and provides a variability measure for at least a portion of the controlled process variables. The process model is used with software run on a computing device to simulate an updated operating point including an updated value or updated range for at least one selected controlled process variable, where the simulation generates a future risk profile and future profit profile for the selected controlled process variable. Risk/profit profile information is generated for the selected controlled process variable from the future risk profile together with information from the future profit profile into at least one of a combined text, numeric, and graphical representation which includes an alarm limit for the selected controlled process variable.

Abstract: A system includes a process controller and an equation evaluation apparatus. The equation evaluation apparatus includes an equation editor, a model factory, and an equation evaluation engine. The equation editor is adapted to receive equations describing a process to be controlled by the process controller. The equation editor is also adapted to generate model information representing the equations. The model factory is adapted to receive the model information and generate an equation stack representing the equations. The equation evaluation engine is adapted to receive evaluation information from the process controller, evaluate at least one of the equations using the evaluation information and the equation stack, and send a result of the evaluation to the process controller. The model information could include information representing algebraic equations, differential equations, algebraic states, differential states, inputs, parameters, constants, and/or expressions.

Abstract: A model predictive controller (MPC) for controlling physical processes includes a non-linear control section that includes a memory that stores a non-linear (NL) model that is coupled to a linearizer that provides at least one linearized model, and a linear control section that includes a memory that stores a linear model. A controller engine is coupled to receive both the linearized model and linear model. The MPC includes a switch that in one position causes the controller engine to operate in a linear mode utilizing the linear model to implement linear process control and in another position causes the controller engine to operate in a NL mode utilizing the linearized model to implement NL process control. The switch can be an automatic switch configured for automatically switching between linear process control and NL process control.

Abstract: A model predictive controller (MPC) for controlling physical processes includes a non-linear control section that includes a memory that stores a non-linear (NL) model that is coupled to a linearizer that provides at least one linearized model, and a linear control section that includes a memory that stores a linear model. A controller engine is coupled to receive both the linearized model and linear model. The MPC includes a switch that in one position causes the controller engine to operate in a linear mode utilizing the linear model to implement linear process control and in another position causes the controller engine to operate in a NL mode utilizing the linearized model to implement NL process control. The switch can be an automatic switch configured for automatically switching between linear process control and NL process control.