Abstract: A method of Key Performance Indicator (KPI) performance analysis and a dynamic Model Predictive Control (MPC) process model for an industrial process including measured variables (MVs) and controlled variables (CVs) for an MPC controller are provided. The MPC process model includes at least one KPI that is also included in a business KPI monitoring system for the industrial process. A future trajectory of the KPI and a steady-state (SS) value for the KPI are estimated. The future trajectory and SS value are used for determining dynamic relationships between key plant operating variables selected from the CVs and MVs, and the KPI. A performance of the KPI is analyzed including identifying at least one cause of a problem in the performance or exceeding the performance during operation of the industrial process from the dynamic relationships and a current value for at least a portion of the MVs.

Abstract: A method of Key Performance Indicator (KPI) performance analysis. A dynamic Model Predictive Control (MPC) process model for an industrial process including measured variables (MVs) and controlled variables (CVs) for an MPC controller is provided. The MPC process model includes at least one KPI that is also included in a business KPI monitoring system for the industrial process. A future trajectory of the KPI and a steady-state (SS) value for the KPI are estimated. The future trajectory and SS value are used for determining dynamic relationships between key plant operating variables selected from the CVs and MVs, and the KPI. A performance of the KPI is analyzed including identifying at least one cause of a problem in the performance or exceeding the performance during operation of the industrial process from the dynamic relationships and a current value for at least a portion of the MVs.

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.