Nonlinear model predictive control for the polymorphic transformation of L‐glutamic acid crystals

Abstract

Polymorphism, a phenomenon where a substance can have more than one crystal forms, has recently become a major interest to the food, speciality chemical, and pharmaceutical industries. The different physical properties for polymorphs such as solubility, morphology, and dissolution rate may jeopardize operability or product quality, resulting in significant effort in controlling crystallization processes to ensure consistent production of the desired polymorph. Here, a nonlinear model predictive control (NMPC) strategy is developed for the polymorphic transformation of L-glutamic acid from the metastable a-form to the stable b-form crystals. The robustness of the proposed NMPC strategy to parameter perturbations is compared with temperature control (T-control), concentration control (C-control), and quadratic matrix control with successive linearization (SL-QDMC). Simulation studies show that T-control is the least robust, whereas C-control performs very robustly but long batch times may be required. SL-QDMC performs rather poorly even when there is no plant-model mismatch due to the high process nonlinearity, rendering successive linearization inaccurate. The NMPC strategy shows good overall robustness for two different control objectives, which were both within 7% of their optimal values, while satisfying all constraints on manipulated and state variables within the specified batch time. V

Advanced control approaches for combined cooling/antisolvent crystallization in continuous mixed suspension mixed product removal cascade crystallizers

“…For model-based approaches, typically a population balance model (PBM) is used to describe the evolution of the CSD in the crystallization process and to obtain open-loop optimal temperature or/and antisolvent addition profiles that can produce desired CSD (Acevedo and Nagy, 2014;Rawlings et al, 1993;Xie et al., 2001;Zhang and Rohani, 2003). In addition, more advanced model-based approaches that solve the open loop optimization repeatedly, such as model predictive control (MPC) has been applied in batch crystallization process (Hermanto et al, 2009;Kalbasenka et al, 2007;Nagy and Braatz, 2003). MPC uses the mathematical model and real time measurements to optimize the current operating curve, based on the predicted future behavior of the system.…”

“…For systems of high complexity and nonlinearity, nonlinear model predictive control (NMPC) is used instead of linear model predictive control (LMPC). Hermanto et al (2009) utilized NMPC strategy to control the polymorphic transformation in a batch crystallization process. It was proved to be more robust than other existing control strategies like temperature control or concentration control.…”

Integrated B2B‐NMPC control strategy for batch/semibatch crystallization processes

“…Totally 200 batches of the simulation runs were used to construct the MPLS model with the number of principle components fine‐tuned as seven by cross‐validation. Incidentally, during the online application, here we assumed that all these state variables in X are either measured or observed by state estimators, such as extended Kalman filter (EKF) or unscented Kalman filter (UKF) . Additional unseen 30 batches were used for validation test as given in Figure , which shows the MPLS model is capable of inferring the kinetic parameters from the provided system dynamic information.…”

“…For the controller implementation, the minimization problem of (7) for conventional B2B was solved by the DE method, while the integrated B2B‐NMPC was transferred to a soft‐constrained problem of (8) and therefore a time‐saving quadratic programming method was conveniently adopted . The tuning parameters for the studied two controllers are listed in Table .…”

“…The NMPC strategy considered here is based on the Just‐in‐Time Learning‐extended prediction self‐adaptive control (JITL‐EPSAC) technique to achieve the desired final product qualities in batch process. The JITL‐EPSAC has improved the original EPSAC method reported in Ref.…”