Abstract: A method includes obtaining information identifying transactions from multiple sources. The transactions relate to multiple functional domains of a supply chain associated with an industrial process. The method also includes storing the information in a data store according to a unified model. The method further includes providing a common user interface for different functional users. The common user interface is configured to display multiple visualizations and reports associated with the transactions. The method also includes obtaining, according to a user input at the common user interface, one or more metrics and one or more analytics from the data store. The one or more metrics and the one or more analytics are associated with the obtained information. The method also includes configuring the common user interface to display, according to the user input, at least one visualization or report involving the one or more metrics and the one or more analytics.

Abstract: A method includes obtaining information identifying transactions from multiple sources. The transactions relate to multiple functional domains of a supply chain associated with an industrial process. The method also includes storing the information in a data store according to a unified model. The method further includes providing a common user interface for different functional users. The common user interface is configured to display multiple visualizations and reports associated with the transactions. The method also includes obtaining, according to a user input at the common user interface, one or more metrics and one or more analytics from the data store. The one or more metrics and the one or more analytics are associated with the obtained information. The method also includes configuring the common user interface to display, according to the user input, at least one visualization or report involving the one or more metrics and the one or more analytics.

Abstract: A method includes obtaining information identifying transactions from multiple sources, the transactions related to multiple functional domains of a supply chain associated with at least one industrial process. The method also includes storing the information in a data store according to a unified model, the unified model representing all of the multiple sources. The method further includes generating one or more user-defined metrics and one or more user-defined analytics using the information. In addition, the method includes storing the metrics and the analytics in the data store.

Abstract: A method of reducing plant emissions includes providing a MPC model for a flaring process including one-to-one models between controlled variables (CVs) including a smoke count and/or a flare count (CV1) and a noise level (CV2), and flow of assist gas as a manipulated variable (MV) and another process gas flow as a disturbance variable (DV). The MPC model receives sensed flare-related parameters during the flaring process including a measure of CV1 (CV1*) and CV2 (CV2*). Provided CV1* is above a minimum setpoint for CV1 (CV1 setpoint) and CV2* is above a setpoint for CV2 (CV2 setpoint), the flaring process is automatically controlled using the MPC model which determines an updated flow setpoint for MV from CV1* and CV2*, the CV1 and CV2 error, and the identified one-to-one models.

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 method of reducing plant emissions includes providing a MPC model for a flaring process including one-to-one models between controlled variables (CVs) including a smoke count and/or a flare count (CV1) and a noise level (CV2), and flow of assist gas as a manipulated variable (MV) and another process gas flow as a disturbance variable (DV). The MPC model receives sensed flare-related parameters during the flaring process including a measure of CV1 (CV1*) and CV2 (CV2*). Provided CV1* is above a minimum setpoint for CV1 (CV1 setpoint) and CV2* is above a setpoint for CV2 (CV2 setpoint), the flaring process is automatically controlled using the MPC model which determines an updated flow setpoint for MV from CV1* and CV2*, the CV1 and CV2 error, and the identified one-to-one models.

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.

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.