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 a plurality of components 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 rapid process model identification technique identifies, in a relatively short period of time, the dynamic relationship between a process input and a process output by developing an estimate of an integrating gain and a process deadtime from the initial response of the process output to a change in the process input. The integrating gain and deadtime values are then used to generate a complete process model for any of many different types of processes. These process models can be used very quickly to perform process simulation or can be used for control purposes, so as to be able to bring a process control system that uses or relies on process models on line much more quickly than was possible in the past. Moreover, this rapid modeling technique can be used to develop simulation models before the controller has completed responding to even a single process upset.

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: 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: A rapid process model identification technique identifies, in a relatively short period of time, the dynamic relationship between a process input and a process output by developing an estimate of an integrating gain and a process deadtime from the initial response of the process output to a change in the process input. The integrating gain and deadtime values are then used to generate a complete process model for any of many different types of processes. These process models can be used very quickly to perform process simulation or can be used for control purposes, so as to be able to bring a process control system that uses or relies on process models on line much more quickly than was possible in the past. Moreover, this rapid modeling technique can be used to develop simulation models for a process loop before the controller that controls the process loop has completed responding to even a single process upset or set point change, making the technique very useful in performing control of slow processes.

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: 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: 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: 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: 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 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.