METHOD AND APPARATUS FOR INTELLIGENT CONTROL AND MONITORING IN A PROCESS CONTROL SYSTEM

20090112335

12/238801

September 26, 2008

A controller includes a control module to control operation of a process in response to control data, a plug-in module coupled to the control module as a non-layered, integrated extension thereof, and a model identification engine. The plug-in detects a change in the control data, and a collects the control data and data in connection with a condition of the process in response to the detected change. The model identification engine executes a plurality of model parameter identification cycles. Each cycle includes simulations of the process each having different simulation parameter values and each using the control data as an input, an estimation error calculation for each simulation based on an output of the simulation and based on the operating condition data, and a calculation of a model parameter value based on the estimation errors and simulation parameter values used in the simulation corresponding to each of the estimation errors.

MEHTA, Ashish (Goa, IN); Wojsznis, Peter (Austin, TX, US); Lewis, Marty J. (Cedar Park, TX, US); Jundt, Larry O. (Round Rock, TX, US); Pettus, Nathan W. (Georgetown, TX, US)

FISHER-ROSEMOUNT SYSTEMS, INC. (Austin, TX, US)

1. An adaptive process control loop controller within a process control environment comprising: a control module to control operation of the process control loop including one or more process control loop devices within the process control loop in response to control data from the control module; a plug-in module operatively coupled to the control module as a non-layered, integrated extension thereof, the plug-in module comprising a process model identification control routine to examine the control data to detect a change therein, and a collection routine to collect the control data and operating condition data in connection with a condition of the process in response to the detected change; and a model identification engine operatively coupled to the plug-in module to receive the collected control data and the collected operating condition data, the model identification engine comprising a model identification routine to execute a plurality of model parameter identification cycles, wherein each cycle comprises: a plurality of simulations of the process based on one or more simulation parameters, each simulation having different simulation parameter values and each simulation using the collected control data as an input, an estimation error calculation for each simulation based on simulated operation condition data in connection with an output of the simulation and based on the collected operating condition data, and a model parameter calculation of a model parameter value based on at least two of the estimation errors and based on simulation parameter values used in the simulation corresponding to each of the at least two estimation errors.

2. The adaptive process control loop controller of claim 1, wherein the model identification engine comprises the model identification routine to iteratively execute each cycle, wherein the plurality of simulations comprise a first, second and third simulation in each cycle, and wherein each subsequent iteration initializes the first simulation with the model parameter value of the preceding iteration of the cycle, and initializes the second and third simulations with the model parameter value of the preceding iteration of the cycle multiplied by a range modifier, wherein the range modifier is decreased with each iteration, and wherein the second and third simulations comprise the upper and lower limits of the process, respectively.

3. The adaptive process control loop controller of claim 1, wherein each cycle corresponds to a different parameter of the model and wherein the model identification engine comprises the model identification routine to sequentially execute the plurality of cycles, with subsequent cycles using the model parameter of one or more of the preceding cycles as a simulation parameter value.

4. The adaptive process control loop controller of claim 1, wherein the process comprises a self regulating process and wherein the model parameter comprises one or more of the group consisting of: a process time constant, a process dead time, a self-regulating process gain.

5. The adaptive process control loop controller of claim 1, wherein the process comprises an integrating process and wherein the model parameter comprises one or more of the group consisting of: a process dead time and an integrating process gain.

6. The adaptive process control loop controller of claim 1, wherein the at least two of the estimation errors comprise the two smallest estimation errors.

7. The adaptive process control loop controller of claim 1, wherein the control data comprises one or more of the group consisting of: a set point change and a controller output.

8. The adaptive process control loop controller of claim 1, wherein a model of the process comprises the model parameter values from the plurality of model parameter identification cycles, and wherein the plug-in module further comprises a model and tuning control routine to calculate a tuning variable for the controller using the model of the process.

9. The adaptive process control loop controller of claim 1, wherein the plug-in module comprises the data collection module to calculate a state variable in connection with the operating state of the process control loop from the control data and operating condition data, and wherein the plug-in module comprises the model and tuning control routine to select the model of the process from a plurality of models of the process based on the calculated state variable.

10. The adaptive process control loop controller of claim 1, wherein the estimation error calculation comprises an integral of a squared error between the simulation output and the collected operating condition data over the collection time of the collected operating condition data.

11. The adaptive process control loop controller of claim 1, wherein the simulation parameter values correspond to the model parameter for which the value is calculated.

12. The adaptive process control loop controller of claim 1, wherein the plug-in module comprises the process model identification control routine to inject the detected change in the controller output to cause the process response to the detected change.

13. A process control system comprising: an adaptive process controller comprising a control module to control operation of a process using control data, a plug-in module to detect changes in the control data and to collect operation condition data in connection with a condition of the process in response to detecting a change in the control data, and a model identification engine to determine one or more model parameter values based on simulation outputs of the process using the control data as simulation inputs and based on the collected operation condition data, wherein a model of the process comprises the one or more model parameter values to model the process; and a workstation operatively coupled to the adaptive process controller, the workstation comprising a server to fetch and store the model of the process from the adaptive process controller, and an operator interface application to enable a user to view, analyze and edit the stored model of the process; wherein the adaptive process controller comprises the model identification engine to retrieve the model of the process from the server in response to a signal from the plug-in module indicating a detected operating state of the process corresponding to the model of the process, and wherein the plug-in module utilizes the model of the process to tune the adaptive process controller.

14. The process control system of claim 12, wherein the workstation comprises the operator interface application to enable a user to utilize the stored model of the process to diagnose and repair a control problem within the process control system.

15. The process control system of claim 12, wherein the plug-in module utilizes the model of the process to tune the adaptive process controller across a plurality of operating regions of the process, wherein each operating region comprises a high limit and a low limit.

16. The process control system of claim 12, wherein the model identification engine is configured to execute: a plurality of simulations of the process based on one or more simulation parameters, each simulation having different simulation parameter values and each simulation using the collected control data as a simulation input and each simulation providing a simulation output, an estimation error calculation for each simulation based on a comparison between the simulation outputs and the collected operating condition data, and a model parameter calculation of a model parameter value based on at least two of the estimation errors and based on simulation parameter values used in the simulation corresponding to each of the at least two estimation errors, wherein the simulation parameter values correspond to the model parameter for which the value is calculated.

17. The process control system of claim 16, wherein the plurality of simulations comprise a first, second and third simulation, and wherein the model identification engine is configured to initialize the first simulation with the model parameter value of the preceding simulation of the process, and initializes the second and third simulations with upper and lower limits of the model parameter of the first simulation of the process, respectively.

18. The process control system of claim 16, wherein the model identification engine is configured to execute the plurality of simulations of the process, the estimation error calculation for each simulation, and the model parameter calculation of a model parameter value for each of a plurality of model parameters.

19. The process control system of claim 18, wherein the process comprises a self regulating process and wherein the plurality of model parameters comprise one or more of the group consisting of: a process time constant, a process dead time and a self-regulating process gain.

20. The process control system of claim 18, wherein the process comprises an integrating process and wherein the model parameters comprises one or more of the group consisting of: a process dead time and an integrating process gain.

21. The process control system of claim 12, wherein the simulation parameter comprises the model parameter for which the value is calculated.

22. The process control system of claim 12, wherein the adaptive process controller comprises a virtual controller, and wherein the workstation comprises the virtual controller.

23. The process control system of claim 12, wherein the adaptive process controller comprises a controller in an online process.

24. The process control system of claim 12, wherein the model identification engine is configured to transmit the model of the process to the workstation based on signal from the plug-in module.

25. The process control system of claim 12, wherein the adaptive process controller is configured to execute a first real-time priority execution thread and second low priority execution thread, wherein the adaptive process controller is configured to execute the control module and the plug-in module in the first real-time priority execution thread, and to execute the model identification engine in the low priority execution thread.

26. A method of controlling a process control system having a plurality of control routines, the method comprising: collecting operating condition data during implementation of the plurality of control routines; detecting an event indicative of a process change in connection with a control routine of the plurality of control routines associated with the process change, wherein the control routine; collecting control data used by the control routine associated with the process change; identifying a process model for the control routine associated with the process change, wherein identifying a process model comprises: executing a plurality of simulations of the process based on one or more simulation parameters, each simulation having different simulation parameter values and each simulation using the collected control data as an input, calculating an estimation error for each simulation based on simulated operation condition data in connection with an output of the simulation and based on the collected operating condition data, calculating a model parameter value based on at least two of the estimation errors and based on simulation parameter values used in the simulation corresponding to each of the at least two estimation errors, and generating a process model from the model parameter values.

27. The method of claim 26, wherein identifying a process model further comprises iteratively executing each cycle, wherein the plurality of simulations comprise a first, second and third simulation in each cycle, and wherein each subsequent iteration comprises: initializing the first simulation with the model parameter value of the preceding iteration of the cycle, initializing the second and third simulations with the model parameter value of the preceding iteration of the cycle multiplied by a range modifier, and decreasing the range modifier with each iteration, wherein the second and third simulations comprise the upper and lower limits of the process, respectively.

28. The method of claim 26, wherein each cycle corresponds to a different parameter of the process model, the method further comprising sequentially executing the plurality of cycles, with subsequent cycles using the model parameter of one or more of the preceding cycles as a simulation parameter value.

29. The method of claim 26, wherein a process model comprises the model parameter values from the plurality of model parameter identification cycles, the method further comprising calculating a tuning variable for the control routine using the process model.

30. The method of claim 26, wherein the collected operating condition data is indicative of a response to an injected parameter change applied to the process.

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edspap.20090112335