Abstract: An integrated optimization and control technique integrates an optimization procedure, such as a linear or quadratic programming optimization procedure, with advanced control, such as model predictive control, within a process plant in which the number of control and auxiliary variables can be greater than the number of manipulated variables within the process plant. The technique first determines a step response matrix defining the correlation between changes in the manipulated variables and each of the process variables that are used during optimization. A subset of the control variables and auxiliary variables is then selected to be used as inputs to a model predictive control routine used to perform control during operation of the process and a square M by M control matrix to be used by the model predictive control routine is generated.

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.

Abstract: An interface or display routine is provided for use in viewing and configuring a function block that performs integrated optimization and control within a process control system. The interface routine may enable a user to view or configure variables, values or other parameters associated with the integrated optimization and control block within the process control system. For example, the interface routine may display the current operating state of the integrated function block, may enable a user to select inputs and output of the function block for use in providing integrated optimization and control, may enable a user to select a particular or desired optimization function for use in the function block, etc. The interface routine may also display the multiple input output curves associated with the optimizer and the controller sections of the integrated function block in a manner that provides ease of view and selection of these curves as part of the algorithm used by the integrated function block.

Abstract: A robust method of creating process models for use in controller generation, such as in MPC controller generation, adds noise to the process data collected and used in the model generation process. In particular, a robust method of creating a parametric process model first collects process outputs based on known test input signals or sequences, adds random noise to the collected process data and then uses a standard or known technique to determine a process model from the collected process data. Unlike existing techniques for noise removal that focus on clean up of non-random noise prior to generating a process model, the addition of random, zero-mean noise to the process data enables, in many cases, the generation of an acceptable parametric process model in situations where no process model parameter convergence was otherwise obtained.

Abstract: A simulation system that includes interconnected simulation blocks which use process models to perform simulation activities for a process plant is integrated into a process control environment for the process plant in a manner that makes the simulation system easy to use and easily updated for on-line process simulation. The disclosed simulation system enables future predicted values as well as the current predicted values of process parameters produced by the simulation system to be made available for performance evaluation as well as to guide plant operations. Additionally, the simulation system is connected to the operating process plant to receive various on-line process plant measurements, and uses these measurements to automatically update the process models used in the simulation system, to thereby keep the simulation system coordinated with the actual operating conditions of the process plant.

Abstract: A system and method for controlling a process includes simulating the process and producing a simulated output of the process, developing a set of target values based on measured inputs from the process and based on the simulated output from the process simulator, and producing multiple control outputs configured to control the process based on the set of target values during each operational cycle of the process control system. The simulated outputs include one or more predicted future values up to the steady state of the process.

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 simulation system that includes interconnected simulation blocks which use process models to perform simulation activities for a process plant is integrated into a process control environment for the process plant in a manner that makes the simulation system easy to use and easily updated for on-line process simulation. The disclosed simulation system enables future predicted values as well as the current predicted values of process parameters produced by the simulation system to be made available for performance evaluation as well as to guide plant operations. Additionally, the simulation system is connected to the operating process plant to receive various on-line process plant measurements, and uses these measurements to automatically update the process models used in the simulation system, to thereby keep the simulation system coordinated with the actual operating conditions of the process plant.

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 method of creating and using an adaptive DMC type or other MPC controller includes using a model switching technique to periodically determine a process model, such as a parameterized process model, for a process loop on-line during operation of the process. The method then uses the process model to generate an MPC control model and creates and downloads an MPC controller algorithm to an MPC controller based on the new control model while the MPC controller is operating on-line. This technique, which is generally applicable to single-loop MPC controllers and is particularly useful in MPC controllers with a control horizon of one or two, enables an MPC controller to be adapted during the normal operation of the process, so as to change the process model on which the MPC controller is based to thereby account for process changes.

Abstract: A state based adaptive feedback/feedforward PID controller includes a model set component, communicatively coupled to a process input, having a state variable defining a number of process regions, and a number of models grouped into the process regions. Each of the grouped models includes a plurality of parameters having a value selected from a set of predetermined initial values assigned to the respective parameter. The adaptive controller further includes an error generator communicatively coupled to the model set component and a process output. The error generator configured to generate a model error signal representative of the difference between a model output signal and a process output signal. The error generator, communicatively coupled to a model evaluation component, is configured to compute a model squared error corresponding to a model and correlating the model squared error to parameter values represented in the model.

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: 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.

Abstract: An integrated optimization and control technique integrates an optimization procedure, such as a linear or quadratic programming optimization procedure, with advanced control, such as model predictive control, within a process plant in which the number of control and auxiliary variables can be greater than the number of manipulated variables within the process plant. The technique first determines a step response matrix defining the correlation between changes in the manipulated variables and each of the process variables that are used during optimization. A subset of the control variables and auxiliary variables is then selected to be used as inputs to a model predictive control routine used to perform control during operation of the process and a square M by M control matrix to be used by the model predictive control routine is generated.

Abstract: An interface or display routine is provided for use in viewing and configuring a function block that performs integrated optimization and control within a process control system. The interface routine may enable a user to view or configure variables, values or other parameters associated with the integrated optimization and control block within the process control system. For example, the interface routine may display the current operating state of the integrated function block, may enable a user to select inputs and output of the function block for use in providing integrated optimization and control, may enable a user to select a particular or desired optimization function for use in the function block, etc. The interface routine may also display the multiple input output curves associated with the optimizer and the controller sections of the integrated function block in a manner that provides ease of view and selection of these curves as part of the algorithm used by the integrated function block.