Advanced State Estimation Techniques for Packed Bed Reactors

Abstract

This work focuses on the evaluation of some of the advanced recursive state estimation techniques applied to packed bed reactors. Tubular, packed or fixed bed reactors are typically modeled as infinite dimensional partial differential equations, also called Distributed Parameter Systems (DPS). Discretization methods such as orthogonal collocation or any other polynomial approximation can be used to convert these DPS into a set of Ordinary Differential Equations (ODE) or Differential Algebraic Equations (DAE), that are more suitable for application of the estimation algorithms. Here we demonstrate the superior performance of the Ensemble Kalman Filter over its other variants (EKF and UKF) for a tubular reactor model. This work also includes the evaluation of the constrained forms of these algorithms for the same tubular reactor model and the results show that the Constrained EnKF which is a nondeterministic approach is able to estimate the states accurately and is less susceptible to numerical errors unlike the Constrained UKF and Constrained EKF which encounter the problem of the covariance matrix becoming negative definite.