Inference of multi-variable trends in unmeasured process quantities

Abstract:
In many applications the trends of unmeasured process quantities are of high interest. Often, these trends can be inferred with high con¯dence from the trends of some measured process quantities. For example, steady-state detection addresses a trend in the unmeasured state variables, but is usually inferred from stationarity of the measurements. This paper provides a method to analyze the con¯dence with which trends in measurements imply trends in unmeasured quantities. Thus, trends of unmeasured quantities can be in- ferred from measured trends with known accuracy. The suggested method, for example, facilitates the selection of a trend error in the measurements to bound the loss of pro¯t in real-time optimization due to deviations from steady-state. Furthermore, the method can also be employed to assess can- didate measurement con¯gurations in a plant with respect to the achievable accuracy of steady-state or more general trend detection.

Keywords:
Process monitoring, real-time optimization, trend detection, steady-state detection

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