OUTPUT ESTIMATION USING MULTIPLE SECONDARY MEASUREMENTS - HIGH-PURITYDISTILLATION

Measurements of temperatures (secondary outputs) and flows (inputs) ar e used to estimate product compositions (outputs) in a distillation co lumn. The problem is characterized by strong collinearity (correlation ) between temperature measurements and an ill-conditioned model from i nputs to outputs. In a linear study, three estimator methods, the Kalm an-Bucy filter, Brosilow's inferential estimator, and principal compon ent regression (PCR), are tested for performance with mu-analysis. One can achieve remarkably good control performance with the static PCR e stimator, which is almost as good as the dynamic Kalman filter. The qu ality of the estimate for these two estimators is improved by addition al temperature measurements, although the improvement is only minor fo r more than about five measurements. On the other hand, the performanc e of the Brosilow inferential estimator may not improve by adding meas urements due to sensitivity to modeling errors. For all estimators, th e use of flow (input) measurements does not improve the estimator perf ormance and does in fact damage the performance if a static estimator is used.