Closed-Loop Identification using Routine Operating Data: the Effect of Time Delay

In industry, in order to store the reams of data that are collected from all the different flow, level, and temperature sensors, the fast-sampled data are very often downsampled before being stored in a data historian. This downsampled or even compressed data are, then, used by process engineers to recover the appropriate process parameters. However, little has been written about the effects of the sampling on the quality of the model obtained. Therefore, in this paper, the effects of sampling time are investigated from both a theoretical and practical perspective using results that come out of the theory of closed-loop system identification with routine operating data. It is shown that, if the ratio between the time delay and sampling time is sufficiently large, then it is possible to recover the true system parameters. The most common industrial processes that fulfill this constraint are temperature control loops. On the other hand, for processes, such as flows, pressures, or levels, with almost no time delay, then the sampling time must be extremely small in order to identify the process parameters. These results suggest that the sampling time has an important bearing on the quality of the model estimated from routine operating data. Using an experimental set-up with a heated tank, the effect of time delay on the identification of the true continuous time parameters was considered for different sampling times. It was shown that increasing the sampling time above a given threshold resulted in identifying an incorrect model.