Detection and Isolation of Batch-to-Batch Parametric Drift in Crystallization Using In-Batch and Post-Batch Measurements Academic Article uri icon

abstract

  • © 2015 American Chemical Society. In this work, we focus on the development of a parametric drift detection and isolation (PDDI) method for the handling of batch-to-batch parametric drifts in a batch crystallization process used to produce hen egg-white (HEW) lysozyme crystals. We consider that the batch crystallization process is controlled by an in-batch model predictive control (MPC) system and is subject to batch-to-batch parametric drifts in the solubility, growth rates, continuous-phase mass and energy balance parameters, and nucleation rate. The proposed PDDI scheme consists of two parts: preparatory stage before batch-to-batch operation and post-batch stage during batch-to-batch operation. The goal of the preparatory stage is to compute the threshold values and signatures for each parametric drift using simulations and batch process common cause variance described by noise. During batch-to-batch operation, the proposed PDDI system monitors closed-loop process residuals, which are computed by taking the difference between the time profiles of the states obtained through in-batch and postbatch measurements from the time profiles of the states obtained from the drift-free simulation with noise. While the measurements of the protein solute concentration and the temperature in the crystallizer are available in real-time, post-batch measurements are usually available for the quality of the crystal products (e.g., number of crystals, average crystal size and shape) and this key characteristic is taken into account in the PDDI method. We then compare the residuals with signatures obtained in the preparatory stage for each parametric drift for isolation of a parametric drift. The PDDI system estimates the magnitude of the parametric drift and updates the parameters of the batch process model used in the in-batch MPC system to compute a set of jacket temperatures for the production of crystals with a desired shape distribution in the next batch. The performance of the MPC with the proposed PDDI scheme is demonstrated by applying it to a multiscale simulation of a batch crystallization process with parametric drifts in the solubility and crystal growth rates. The closed-loop system simulations demonstrate that crystals with a crystal shape distribution that is closer to a desired set-point value are produced under a parametric-drift handling scheme that integrates the in-batch MPC with the proposed PDDI system compared to those under the MPC with the nominal process model.

altmetric score

  • 0.25

author list (cited authors)

  • Kwon, J., Nayhouse, M., & Christofides, P. D.

citation count

  • 4

publication date

  • May 2015