Chapter 11 - Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation of a continuous reactive distillation column with optimal sensor configuration

Abstract

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 chapter, the dynamic state sensitive measurement information extracted from the nonlinear reactive distillation process is employed to configure the measurement specific empirical observability grammians for sensors selection. These grammians are subjected to various scalar quantification measures to find the degree of observability in order to select the best set of temperature sensors for state estimation in the process. These optimally selected temperature measurements are then incorporated in a process model based state estimation scheme for reliable estimation of distillation product compositions. The validity of the measurement sensors selected by the grammian quantification measures are further ascertained by evaluating the estimator performance for various nonoptimal measurement combinations. The results on application to a metathesis reactive distillation column demonstrate the usefulness of the measurement specific empirical observability grammian based sensor selection strategy for inferential state estimation of compositions in the reactive distillation process.