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 are methods and devices for controlling a process with a control signal. Iterations of a control routine are implemented to generate the control signal, and when an indication of a response to the control signal is unavailable, a feedback contribution to the control signal is maintained over one or more of the iterations of the control routine. The feedback contribution is then modified upon receiving the response indication, in which the feedback contribution is determined in accordance with an elapsed time between the received response indication and a previous communication of the response indication.

Abstract: Disclosed is a controller having a processor and a control module adapted for periodic execution by the processor and configured to be responsive to a process variable to generate a control signal for a process. An iteration of the periodic execution of the control module involves implementation of a routine configured to generate a representation of a process response to the control signal. The routine is further configured to maintain the representation over multiple iterations of the periodic execution of the control module and until an update of the process variable is available. In some cases, the update of the process variable is made available via wireless transmission of the process signal. In those and other cases, the controller may be included within a process control system having a field device to transmit the process signal indicative of the process variable non-periodically based on whether the process variable has changed by more than a predetermined threshold.

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 first principles model may be used to simulate a batch process, and the first principles model may be used to configure a multiple-input/multiple-output control routine for controlling the batch process. The first principles model may generate estimates of batch parameters that cannot, or are not, measured during operation of the actual batch process. An example of such a parameter may be a rate of change of a component (e.g., a production rate, a cell growth rate, etc.) of the batch process. The first principles model and the configured multiple-input/multiple-output control routine may be used to facilitate control of the batch process.

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: Disclosed are systems and methods for on-line monitoring of operation of a process in connection with process measurements indicative of the operation of the process. In some cases, the operation of the process is simulated to generate model data indicative of a simulated representation of the operation of the process and based on the process measurements. A multivariate statistical analysis of the operation of the process is implemented based on the model data and the process measurements. The output data from the multivariate statistical analysis may then be evaluated during the operation of the process to enable the on-line monitoring of the process involving, for instance, fault detection via classification analysis of the output data.

Abstract: Graphic displays, which display information about process elements and the manner in which these elements are connected within a process, process modules, which simulate the operation of the elements depicted within the graphic displays and control modules, which perform on-line control activities within a process, may be communicatively connected together to provide a combined control, simulation and display environment that enables enhanced control, simulation and display activities. Smart process objects, which have both graphical and simulation elements, may used to create one or more graphic displays and one or more process simulation modules, each having elements which may communicate with one another to share data between the graphic displays and the process modules.

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: Smart process objects, which have both graphical and simulation elements, may be used to create one or more graphic displays and one or more process simulation modules, each having elements which may communicate with one another and with devices within a process plant to model and depict the operation of a process plant. The smart process objects may include one or more device objects, which represent physical devices within the process plant, and may include one or more smart connection objects which represent and model the flow of a material, such as a gas, a liquid, a composition of solid, electricity, etc., through a connection between entities within the process plant. The smart process objects may also include one or more smart stream objects, which also may represent and model the flow of a material at a particular point in the process plant.

Abstract: A process control system includes economic models disposed in communication with process control modules, as well as with sources of economic data, such as cost, throughput and profit data, and uses the economic models to determine useful economic parameters or information associated with the actual operation of the process plant at the time the plant is operating. The economic models can be used to provide financial statistics such as profitability, cost of manufactured product, etc. in real time based on the actual current operating state of the process and the business data associated with the finished product, raw materials, etc. These financial statistics can be used to drive alarms and alerts within the process network and be used as inputs to process plant optimizers, etc. to provide for better or more optimal control of the process and to provide a better understanding of the conditions which lead to maximum profitability of the plant.

Abstract: Graphic displays, which display information about process elements and the manner in which these elements are connected within a process, process modules, which simulate the operation of the elements depicted within the graphic displays and control modules, which perform on-line control activities within a process, may be communicatively connected together to provide a combined control, simulation and display environment that enables enhanced control, simulation and display activities. Smart process objects, which have both graphical and simulation elements, may used to create one or more graphic displays and one or more process simulation modules, each having elements which may communicate with one another to share data between the graphic displays and the process modules.

Abstract: A simulation system attaches to a configuration database for one or more fully configured nodes of a process control system and copies the modules within those nodes into one or more simulation computers as simulation modules. The simulation system includes an algorithm that, during or after the copying process, automatically creates a reference parameter module for each of the copied nodes that stores a reference parameter for each of the inputs, outputs or other references to signals external to the nodes, such as input/output blocks, other function blocks in other nodes, transmitter blocks in field devices, etc.

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.