Abstract
The comparative study of static estimators (soft sensors) for a multicomponent distillation process based on the industrial and calibrated rigorous model datasets is considered. The sequence of distillation columns of an FFC unit is analyzed as an industrial case study. The contribution of the paper is to develop a method aimed at incorporating a priori knowledge about process in terms of rigorous models for static estimator design when the training sample is small and contains measurement errors. The superiority of a constrained optimization approach for SE design over conventional robust M -estimator is reported. The system of constraints is derived from a calibrated rigorous model of an industrial plant.
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