Protein Crystal Shape and Size Control in Batch Crystallization: Comparing Model Predictive Control with Conventional Operating Policies

Abstract

In this paper, we focus on a batch protein crystallization process used to produce tetragonal hen egg white lysozyme crystals and present a comparative study of the performance of a model predictive control (MPC) strategy formulated to account for crystal shape and size distribution with conventional operating strategies used in industry, namely, constant temperature control (CTC) and constant supersaturation control (CSC). Initially, a comprehensive, batch crystallizer model is presented involving a kinetic Monte Carlo (kMC) simulation model which describes the nucleation and crystal growth via adsorption, desorption, and migration mechanisms on the (110) and (101) faces and mass and energy balances for the continuous phase, which are developed to estimate the depletion in the protein solute concentration and the variation in the crystallizer temperature. Existing experimental data are used to calibrate the crystal growth rate and to develop an empirical expression for the nucleation rate. Simulation results demonstrate that the proposed MPC, adjusting the crystallizer jacket temperature, is able to drive the crystal shape to a desired set-point value with a low polydispersity for crystal size compared to CTC and CSC operating policies, respectively. The proposed MPC determines the optimal operating conditions needed to obtain protein crystals of a desired shape and size distribution as it helps avoid the small crystal fines at the end of the batch run.

Modeling and control of ibuprofen crystal growth and size distribution

“…In this section, a model predictive controller (MPC) is presented for seeded batch ibuprofen crystallization control. MPC is used in order to provide optimality, robustness, and constraint handling in the batch crystallization process (Shi et al, 2006(Shi et al, , 2005Kwon et al, 2014). In particular, the objective of the MPC will focus on minimizing the crystal size distribution by computing a set of optimal jacket temperatures over the length of the prediction horizon.…”

Protein crystal based materials for nanoscale applications in medicine and biotechnology

“…They have subsequently written an expert opinion piece focusing on the use of protein crystals for the delivery of biopharmaceuticals, which describes the benefits, challenges, and techniques associated with this technology (Basu et al, ). In addition to this work, experimental (Falkner et al, ; Hebel, Huber, Stanislawski, & Hekmat, ; Hekmat, ; Martin & Zilm, ) and computational (Shi, Mhaskar, El‐Farra, & Christofides, ; J. J. Liu, Ma, Hu, & Wang, , ; Nayhouse, Sang‐Il Kwon, Christofides, & Orkoulas, ; J. J. Liu, Hu, & Wang, ; Joseph Sang‐Il Kwon, Nayhouse, Christofides, & Orkoulas, , , ) studies have characterized batch crystallization techniques for the generation of monodisperse sub‐micron protein crystals of specific morphologies in the interest of industrial scale pharmaceutical formulations and drug delivery. We also note that microcrystalline suspensions offer potential advantages in terms of shelf‐stability (Shenoy et al, ) and decreased viscosity which could enable delivery via smaller needles (Basu et al, ).…”

Application of offset‐free Koopman‐based model predictive control to a batch pulp digester

“…However, these studies have a crucial limitation; specifically, only the evolution of macroscopic properties (i.e., temperature and concentration of components in wood chip and liquor phases) is studied without considering the microscopic properties of fibers (i.e., pore size, porosity, cell wall thickness [CWT], and fiber length), which directly influence the delignification rate in pulp digesters 17 and physical properties of paper products such as density, strength, and absorbability 18,19 . To handle this limitation, Choi and Kwon 20 developed a multiscale modeling framework for pulp digesters that combines a kinetic Monte Carlo (kMC) model 21–24 with the extended Purdue model, 25 which is the most commonly used macroscopic model for pulp digesters, to describe the evolution of microscopic attributes of fibers as well as that of macroscopic phenomena during pulping. Based on this multiscale modeling framework, Choi and Kwon 26 developed a multiscale model that tracks the CWT value of fibers and considers the fiber collapse phenomenon.…”