Development and implementation of a high-performance sensor system for an industrial polymer reactor

  • Pottmann M
  • Ogunnaike B
  • Schwaber J
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Abstract

Because of increasingly stringent demands on product quality, process
measurements of high precision, accuracy, and reliability are required
in many processes where process variables are traditionally difficult to
measure. In many applications, several different measurements of certain
key process variables are available from various sources, including
on-line analyzers, lab analyses, inferential measurements, and models of
all types. Because these individual sensors often differ significantly
in terms of their static and dynamic characteristics, precision,
accuracy, sampling rate, measurement time delay, etc., in practice, only
one of them is utilized for process monitoring and control purposes; the
other sensor measurements are ignored. However, significantly improved
estimates of the process variables can be obtained by utilizing all of
the available sensor information simultaneously, i.e., by combining
(fusing) these dissimilar measurements into a single estimate in a
robust and fault-tolerant fashion. There is evidence that such sensor
fusion operations are integral to the operation of biological control
systems, and it is from one of these systems (the blood pressure control
system) that we have drawn inspiration for developing the technique
discussed in this article. A sensor fusion approach is developed using
stochastic systems theory (in particular, Kalman filtering). The
advantages of using multiple, redundant sensors, as well as the
potential improvements achievable by combining delayed or multirate
sensor information with that of a single nominal sensor, are then
quantifiled. Robustness is achieved by augmenting the nominal technique
with failure detection, classification, and compensation schemes, based
on statistical hypothesis testing. The performance of the techniques is
demonstrated on an actual plant application in which improved feed
composition estimates are obtained for an industrial ethylene
copolymerization reactor.

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Authors

  • Martin Pottmann

  • Babatunde A. Ogunnaike

  • James S. Schwaber

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