Classifications

• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
  • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
  • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
  • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
  • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B17/00—Systems involving the use of models or simulators of said systems
  • G05B17/02—Systems involving the use of models or simulators of said systems electric

Definitions

• the present invention relates to an automatic online calibration of input/output models.
• QE Quality Estimators
• QE's are usually identified from collected process data.
• a QE has to be calibrated using historic quality measurements, which can be taken on-line or off-line depending on the type of process and/or the type of measurement envisaged, so as to minimize, or preferably avoid, any drift in the predicted quality.
• QE's are preferably used in situations which allow rather infrequent and/or delayed measurements of product quality. This may be the case when, for instance, the amount of time needed to produce the measured value is rather long or when the method is relatively costly.
• the Robust Quality Estimator (RQE) provides a more accurate and robust quality prediction which improves the performance of any quality control scheme in which it is applied. For instance, it improves the performance of a linear model predictive controller when the process is such that the steady-state gains and/or the dynamics (such as the deadtime) between the manipulated variables and the controlled quality are varying in an unpredictable manner within certain identified boundaries. Moreover, is can also be used to facilitate closed-loop control of any process variable with a difficult dynamic behaviour.
• the present invention therefore relates to a method for automatic on-line calibration of process models for real-time prediction of process quality from raw process measurements which method comprises the steps of collecting raw process data, processing data collected through a mathematical model to obtain a prediction of the quality, processing this prediction through two independent dynamic transfer functions thus creating two intermediate signals, storing the two intermediate signals obtained as a function of time in history, retrieving, at the time of a real and validated measurement of the quality, from the history the absolute minimum and maximum values of the two intermediate signals in the time period corresponding to a minimum and maximum specified deadtime, which values define the minimum and maximum prediction possible, calculating the deviation as being the difference between the real and validated measurement and the area encompassed between the minimum and maximum prediction possible as obtained, and repeating these steps if the absolute value of the deviation obtained is zero, or, if the absolute value of the deviation obtained is larger than zero, incorporating the deviation into the process model and repeating the steps.
• the collection of raw process data to be used in the method according to the present invention can be carried out by methods known in the art. It is customary in process control technology to measure data at a number of points over a period of time. For instance, in refining operations, operating parameters such as temperature, pressure and flow are normally measured at frequent intervals, or even in a continuous manner and they can be stored and processed in many ways as is known to those skilled in the art.
• An essential step in the method for automatic on-line calibration is the calculation of the minimum and maximum prediction possible at the time of the real and validated measurement(s) of the quality.
• This can be achieved by applying two independent dynamic transfer functions (so-called uncertain dynamics) to the undelayed real time, thus creating two (independent) intermediate signals.
• uncertain dynamics independent dynamic transfer functions
• These intermediate signals are stored as a function of time in history. This will result in essence in an (uncertainty) area in which the actual process response should be placed and which will become very narrow when reaching the steady-state situation. It is also possible that the uncertainty area is reduced to a line corresponding to the event in which the two independent dynamic transfer functions are identical.
• the so-called minimum and maximum prediction possible are obtained by calculating from the history the absolute minimum and maximum values of these two intermediate signals in the time period corresponding to a minimum and maximum specified deadtime.
• the area Before reaching the steady-state situation, the area can be very wide.
• the state of the art systems will either only calibrate during steady-state or have the risk of making a false calibration in case the real and validated measurement(s) of the quality is within the above mentioned area.
• the method according to the present invention is specifically designed to calibrate only when the real and validated measurement(s) of the quality are outside the uncertainty area, thus preventing instabilities in closed-loop.
• the calibration process is carried out by calculating the deviation (so-called prediction error) as being the distance between the real and validated measurement and the area encompassed between the minimum and maximum prediction possible as obtained from the earlier calculation.
• the deviation found will not be used as further input in the calibration process but the system will continue by repeating the steps carried out up till now as there is no need to refine the system. If, however, the deviation as calculated shows that the absolute value of the deviation is larger than zero, the deviation obtained will be incorporated into the process model and the previous steps will be repeated. The net result will be the generation of a modified, more precise, prediction model which will then serve as the basis for further modifications depending on the level of deviations being observed during the course of the calibrating process.
• Kalman filter When incorporation of the allowed deviation into the process model is envisaged with the use of a Kalman filter the result will be that the deviation can be incorporated into the mathematical model by adjusting its linear parameters thereby updating the prediction band and improving the mathematical model.
• the use of a Kalman filter is well known in the art of process control operations. Reference is made in this respect to “Stochastic Processes and Filtering Theory” by Jazwinski (Academic Press, Mathematics and Science and Engineering, Vol. 64, 1970). Since Kalman filters are, in essence, optimal stochastic filters, they also filter out, or even eliminate, the noise on the measured quality which makes them very suitable for use in the method according to the present invention.
• Kalman filters is not limited to calibration operations which are carried out under non steady-state conditions as it is equally capable of providing useful information when a process is being operated under steady-state conditions. Under such conditions it has the additional advantage that it will reduce the prediction error in the future which makes the QE part of a learning system which is upgrading itself when applied in practice.
• the calibration process as described in the present invention can be extrapolated for robust multivariable predictive controllers to cover uncertain dynamics in the control model for all the transfer functions between the manipulated variables and the controlled variables.

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• Engineering & Computer Science (AREA)
• Artificial Intelligence (AREA)
• Physics & Mathematics (AREA)
• Automation & Control Theory (AREA)
• Evolutionary Computation (AREA)
• General Physics & Mathematics (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Software Systems (AREA)
• Medical Informatics (AREA)
• Health & Medical Sciences (AREA)
• Feedback Control In General (AREA)
• Management, Administration, Business Operations System, And Electronic Commerce (AREA)
• Testing, Inspecting, Measuring Of Stereoscopic Televisions And Televisions (AREA)

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• 2001
  • 2001-06-27 US US09/892,417 patent/US20020019722A1/en not_active Abandoned
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  • 2001-07-17 RU RU2003104831/09A patent/RU2280270C2/ru active
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