Multivariate Statistical Monitoring in an Industrial SBR Process. Part II: Fault Detection and Diagnosis

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

Rosario, Argentina

Congreso; XIII Reunión de Trabajo en Procesamiento de la Información y Control (RPIC2009); 2009

Comite Organizador

The production of Styrene-Butadiene Rubber (SBR) in an industrial continuous 7-reactor train operated under steady-state (SS) conditions is investigated. On-line monitoring of quality variables in the SBR process is difficult because of the lack of specific sensing devices. In Part I of this work, a soft-sensor for monitoring both process and quality variables was developed. It was based on a partial least squared (PLS) regression, and then it was validated and compared with a previously-developed neural network soft sensor. In this Part II, we present a PLS-based algorithm for the detection, isolation, and characterization of several faults that frequently disturb the SS operation of the reaction train. Two sources of errors that produce erroneous SS operations are considered: 1) faults in the mass- or volume-flow sensors; and 2) the presence of unmeasured disturbances (e.g., impurities in some reagents). Simulation results show that the proposed algorithm adequately identify the analyzed faults and/or disturbances. The results suggest that the proposed methods are powerful tools for monitoring and control the normal SS operation of the SBR process.