State estimation with delayed measurements is essential to the operation of polymer processes due to the limited availability of reliable online sensors and the unavoidable hold‐up time in the acquisition of critical variables data. In this work, a two‐timescale approach is applied to three filters based on the Unscented Transformation, the Unscented Kalman Filter, the Unscented Recursive Nonlinear Dynamic Data Reconciliation and the Reformulated Constrained Unscented Kalman Filter, in order to incorporate delayed measurements into their estimation scheme. A comprehensive comparative analysis is performed, which shows that the three of them have very good accuracy and convergence properties. However, the Unscented Kalman Filter performs better in terms of computational time.

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