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Soft Sensor Design Using PLS and Neural Networks (NN): Comparison and Industrial Application Arun K. Tangirala, CPC Group, U of A CPC Group Seminar, February 10, 1999 (CME 572, 12:00 p.m.) Abstract
| The Melt Flow Index (MFI) provides a convenient measure to
characterize polymer processibility. In the polymer manufacturing
operations, it also serves as an indicator for product quality control.
Mitsubishi Chemical Corporation makes several polymer grades in their
Mizushima plant using the high pressure, high temperature autoclave
technology. In this work, we summarize the results of a
university-industry collaboration relating to the development of a soft
sensor for the MFI of the polymers produced. Partial Least Squares (PLS)
and Neural Nets (NN) are employed to obtain an estimate of the
infrequently available MFI measurements from the routinely available
process measurements. A comparative study of both these techniques in
issues such as quality of predictions, the ease of modelling, etc. is
presented. |