Soft-sensor for Monitoring an Industrial Styrene-Butadiene Rubber Process (SBR)

GODOY, J.L.; VEGA, J.R.; MARCHETTI, J.L.

Girona

Congreso; CAIP’2011 – 10º Congreso Interamericano de Computación Aplicada a la Industria de Procesos; 2011

This work presents a full methodology to build and evaluate a soft sensor capable of monitoring the production of Styrene-Butadiene Rubber 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. 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 cannot 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 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.