Handling Parametric Drift in Batch Crystallization Using Predictive Control with R2R Model Parameter Estimation∗∗Financial support from the Extreme Science and Engineering Discovery Environment (TG-CCR120003), the National Science Foundation (CBET-0967291), and the NSF Graduate Research Fellowship (DGE-1144087) given to Michael Nayhouse is gratefully acknowledged.

In this work, we develop a run-to-run (R2R) model parameter estimation scheme based on moving horizon estimation (MHE) concepts for the modeling of batch-to-batch process model parameter variation using a polynomial regression scheme in a moving horizon fashion. Then, a model predictive controller (MPC) with the proposed parameter estimation scheme is applied to a kinetic Monte Carlo (kMC) simulation model of a batch crystallization process used...