Monitoring and fault diagnosis of a polymerization reactor by interfacing knowledge-based and multivariate SPM tools

Abstract

An intelligent process monitoring and fault diagnosis environment is developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring continuous multivariable process operation. The software is tested by monitoring the performance of a continuous stirred tank reactor for polymerization of vinyl acetate. The real-time KBS G2 and its diagnostic assistant (GDA) tool are integrated with MSPM methods based on canonical variate state space (CVSS) process models. Fault detection is based on T/sup 2/ of state variables and squared prediction errors (SPE) charts. Contribution plots in G2 are used for determining the process variables that have contributed to the out-of-control signal indicated by large T/sup 2/ and/or SPE values, and GDA is used to diagnose the source cause of the abnormal process behavior. The MSPM modules developed in Matlab are linked with G2 and GDA, permitting the use of MSPM tools for multivariable processes with autocorrelated data. The presentation will focus on the structure and performance of the integrated system. On-line SPM of the multivariable polymerization process is illustrated by simulation studies.