Abstract: A method of diagnosing an adaptive process control loop includes measuring process control loop signal data, generating a plurality of process control loop parameters from the process loop signal data and evaluating a condition of the adaptive process control loop from one or more of the plurality of process control loop parameters. The process control loop data is generated as a result of a normal operation of one or more process control devices within the adaptive process control loop when the adaptive process control loop is connected on-line within a process control environment. A self-diagnostic process control loop includes a diagnostic tool adapted to receive a diagnostic index pertaining to a process control loop parameter for each component of the process control loop and for the complete process control loop. Each diagnostic index is generated from signal data by a corresponding index computation tool.

Abstract: A process control system integrates the collection and analysis of process control data used to perform certain computationally expensive process control functions, like adaptive model generation and tuning parameter generation, in the same control device in which one or more of the process control routines are implemented, to thereby provide for faster and more efficient support of the process control routines. This system replaces a layered approach using multiple processing devices by integrating an analytical server which performs computationally expensive analyses used by one or more control routines directly into the real-time control device in which the one or more control routines are located. This integration provides the ability to analyze large quantities of data for multiple process loops controlled by a particular device in a fast and efficient manner.

Abstract: Disclosed is a method of controlling and managing a process control system having a plurality of control loops. The method includes implementing a plurality of control routines to control operation of the plurality of control loops, respectively. The plurality of control routines may include at least one non-adaptive control routine. Operating condition data is then collected in connection with the operation of each control loop of the plurality of control loops, and a respective process model is identified for each control loop of the plurality of control loops from the respective operating condition data collected for each control loop of the plurality of control loops. In some embodiments, the identification of the respective process models may be automatic as a result of a detected process change or be on-demand as a result of an injected parameter change.

Abstract: A process control configuration system is provided for use in creating or viewing an integrated optimization and control block that implements an optimization routine and a multiple-input/multiple-output control routine. The configuration system may enable a user to display or configure the optimizer or the control routine. A storage routine may store information pertaining to a plurality of control and auxiliary variables and to a plurality of manipulated variables to be used by the optimization routine and/or the control routine. A display routine may present a display to a user regarding the information pertaining to the plurality of control and auxiliary variables and to the plurality of manipulated variables.

Abstract: An adaptive multivariable process control system includes a multivariable process controller, such as a model predictive controller, having a multivariable process model characterized as a set of two or more single-input, single-output (SISO) models and an adaptation system which adapts the multivariable process model. The adaptation system detects changes in process inputs sufficient to start an adaptation cycle and, when such changes are detected, collects process input and output data needed to perform model adaptation. The adaptation system next determines a subset of the SISO models within the multivariable process model which are to be adapted, based on, for example, a determination of which process inputs are most correlated with the error between the actual (measured) process output and the process output developed by the multivariable process model. The adaptation system then performs standard or known model switching and parameter interpolation techniques to adapt each of the selected SISO models.

Abstract: A process control configuration system is provided for use in creating or viewing an integrated optimization and control block that implements an optimization routine and a multiple-input/multiple-output control routine. The configuration system may enable a user to display or configure the optimizer or the control routine. A storage routine may store information pertaining to a plurality of control and auxiliary variables and to a plurality of manipulated variables to be used by the optimization routine and/or the control routine. A display routine may present a display to a user regarding the information pertaining to the plurality of control and auxiliary variables and to the plurality of manipulated variables.

Abstract: An optimization technique for use in driving a process plant controller, such as a model predictive controller, uses an organized, systematic but computationally simple method of relaxing or redefining manipulated, control and/or auxiliary variable constraints when there is no feasible optimal solution within pre-established constraints, to thereby develop an achievable solution for use by the controller. The optimization routine uses penalized slack variables and/or redefines the constraint model in conjunction with the use of penalty variables to develop a new objective function, and then uses the new objective function to determine a control solution that bests meets the original constraint limits.