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 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 method and system for process control using a model predictive controller. The control system can have one or more control devices operably coupled to a processing system for controlling a process of the processing system; a modeling tool to provide a non-linear model based at least in part on the process and to provide a plurality of linearized models based at least in part on the non-linear model, where the plurality of linearized models are linearized at different linearization rates; and a controller operably coupled to the modeling tool. The controller can select one of the plurality of linearized models based on a comparison of the plurality of linearized models with a reference model. The controller can send one or more control signals to at least one of the one or more control devices. The one or more control signals can be determined using the selected one of the plurality of linearized models.

Abstract: A method and system for process control. The control system can be operably coupled to a processing system. The control system can include control devices operably coupled to the processing system; a modeling module to provide a linear model based at least in part on the processing system; a computational module to provide controller algorithms associated with the control devices; a user interface module to present at a user interface controller information based at least in part on the linear model and the controller algorithms; and a separate coordination module for establishing communication between the modeling module, the computational module and the user interface module. One or more control signals can be provided to at least one of the control devices for controlling the processing system. In one embodiment, the modeling module can generate the linear model from a non-linear process.

Abstract: An apparatus, method, and computer program for stiction compensation in a process control system are provided. A determination is made as to whether a valve is suffering from stiction. A control signal provided to the valve is adjusted in order to at least partially compensate for the stiction suffered by the valve. Adjusting the control signal could include (i) adjusting the control signal to cause the valve to move into a steady-state position and (ii) adjusting the control signal to cause the valve to remain in the steady-state position. Adjusting the control signal to cause the valve to move into the steady-state position could include (a) adjusting the control signal to cause the valve to move from a current position into a new position and (b) adjusting the control signal to cause the valve to move from the new position into the steady-state position.

Abstract: A method and system for process control using a model predictive controller. The control system can have one or more control devices operably coupled to a processing system for controlling a process of the processing system; a modeling tool to provide a non-linear model based at least in part on the process and to provide a plurality of linearized models based at least in part on the non-linear model, where the plurality of linearized models are linearized at different linearization rates; and a controller operably coupled to the modeling tool. The controller can select one of the plurality of linearized models based on a comparison of the plurality of linearized models with a reference model. The controller can send one or more control signals to at least one of the one or more control devices. The one or more control signals can be determined using the selected one of the plurality of linearized models.

Abstract: A method and system for process control. The control system can be operably coupled to a processing system. The control system can include control devices operably coupled to the processing system; a modeling module to provide a linear model based at least in part on the processing system; a computational module to provide controller algorithms associated with the control devices; a user interface module to present at a user interface controller information based at least in part on the linear model and the controller algorithms; and a separate coordination module for establishing communication between the modeling module, the computational module and the user interface module. One or more control signals can be provided to at least one of the control devices for controlling the processing system. In one embodiment, the modeling module can generate the linear model from a non-linear process.

Abstract: A multi-dimensional database query to be sent to a data source may be split into multiple smaller queries by a mid-tier solution and sent individually to the data source(s) for calculation and retrieval of data. This may be accomplished by creating row and column tree structures representing title and header rows and header columns in the query grid. The operation to be performed by the query may then be performed on these tree structures. The grid may then be split based on the resulting tree structures, and forwarded independently to one or more data servers. The results may be merged to generate the complete result or cursored. This allows the system to pass multiple smaller grids to data servers.

Abstract: An apparatus, method, and computer program for stiction compensation in a process control system are provided. A determination is made as to whether a valve is suffering from stiction. A control signal provided to the valve is adjusted in order to at least partially compensate for the stiction suffered by the valve. Adjusting the control signal could include (i) adjusting the control signal to cause the valve to move into a steady-state position and (ii) adjusting the control signal to cause the valve to remain in the steady-state position. Adjusting the control signal to cause the valve to move into the steady-state position could include (a) adjusting the control signal to cause the valve to move from a current position into a new position and (b) adjusting the control signal to cause the valve to move from the new position into the steady-state position.