Multivariate Statistical Monitoring of an Industrial SBR Process.

GODOY, JOSÉ L; MINARI, ROQUE J.; VEGA, JORGE R.; MARCHETTI, JACINTO L

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

ELSEVIER SCIENCE BV

Año: 2011 vol. 107 p. 258 - 268

0169-7439

This work presents a full methodology to build and evaluate a soft sensor capable of monitoring the production of Styrene-Butadiene Rubber (SBR) in an industrial train of 7 continuously-stirred tank reactors. The aim is to develop a device for on-line estimation of production and quality variables using a multivariate statistical technique like partial least squared (PLS). Besides pursuing the soft sensor development, this paper attempts to provide a guide for similar developments by suggesting attention to several specific methodological points. Since a wide range validation space is desired for this sensor and the actual plant can not be arbitrarily disturbed, an existing complex fundamental model is used to explore different possible operating conditions. The approach used to develop the soft sensor includes a distributed sampling of multivariate steady-state (SS) conditions to collect the calibration data set, and the use of a filter for excluding outliers. Then, it is shown that the analysis of how the explained variability progress when latent variables are included in the model allows the detection of poor predictor variables providing the chance for improving the multivariable regression by eliminating interfering contributions. Few after-modeling techniques are also suggested to confirm the consistency of the calibrating data set and the model precision over the applicability domain.