Optimal selection of sensors for state estimation in a reactive distillation process

The success of state estimation in a high dimensional system like multicomponent reactive distillation depends on the rigorous evaluation of the observability and appropriate selection of measurements that adequately characterize the process behavior. In this work, the dynamic state sensitive measurement information extracted from the nonlinear reactive distillation process is employed to configure the gramian covariance matrices which are then subjected to various scalar quantification measures to find the degree of observability in order to select the temperature sensors for state estimation in the process. These optimally configured process measurements are then incorporated in a process model based composition estimation scheme. The validity of the sensors that are selected by the gramian quantification measures are further ascertained through the evaluation of the estimator performance for various nonoptimal measurement combinations. The results on application to a metathesis reactive distillation column exhibit the usefulness of the empirical observability gramian based sensor selection strategy for inferential state estimation of reactive distillation process.