On-line calibration process

Method for automatic on-line calibration of process models for real-time prediction of process quality from raw process measurements by 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. By using a Kalman filter method for incorporating the deviation into the mathematical model its linear parameters will be updated, thereby improving the model. The calibration process with the Kalman filter can be applied under non steady-state conditions.

DESCRIPTION OF THE PREFERRED EMBODIMENT 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. In order to get a prediction of the quality out of the raw process data collected, a mathematical model will be used. Examples of mathematical models suitable for QE are systems known in the art as Multiple Linear Regression, Linear Dynamic Model (in the Laplace transform Domain) and Radial Bias Function Neural Network (optionally with Gaussian function) Depending on the nature of the process model applied and the type of raw material data received, those skilled in the art will select the type of QE best fitting the perceived goal. 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. 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. 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, however, 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. In the method according to the present invention 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. If the calculation of the deviation as described herein above shows that the absolute value of the deviation obtained is zero, meaning that the validated and real measurement of the quality is within the uncertainty area, 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. 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. It should be noted that the use of 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. In the event that no real and validated measurement of the quality is received, calibration as defined in steps e, f and g of the claims is not carried out. The system will repeat steps a-d of the claims until a further real and validated measurement of the quality is received. 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.