Robust optimal control of polymorphic transformation in batch crystallization

AIChE Journal (Impact Factor: 2.75). 10/2007; 53(10):2643 - 2650. DOI: 10.1002/aic.11266

ABSTRACT One of the most important problems that can arise in the development of a pharmaceutical crystallization process is the control of polymorphism, in which there exist different crystal forms for the same chemical compound. Different polymorphs can have very different properties, such as bioavailability, which motivates the design of controlled processes to ensure consistent production of the desired polymorph to produce reliable therapeutic benefits upon delivery. The optimal batch control of the polymorphic transformation of L-glutamic acid from the metastable α-form to the stable β-form is studied, with the goal of optimizing batch productivity, while providing robustness to variations in the physicochemical parameters that can occur in practice due to variations in contaminant profiles in the feedstocks. A nonlinear state feedback controller designed to follow an optimal setpoint trajectory defined in the crystallization phase diagram simultaneously provided high-batch productivity and robustness, in contrast to optimal temperature control strategies that were either nonrobust or resulted in long-batch times. The results motivate the incorporation of the proposed approach into the design of operating procedures for polymorphic batch crystallizations. © 2007 American Institute of Chemical Engineers AIChE J, 2007

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Available from: Richard D Braatz, Sep 28, 2015
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    • "The underlying principle is that changing the global properties can change the dynamic pathways of assembly. For example, during crystallization, optimized temperature control can improve the yield of one particular crystal polymorph over another [33] [34]. The number of such global variables that can be useful for altering the state of assembly are somewhat limited, which often includes temperature, pressure, concentration, composition, and, for some systems, external fields such as electric and magnetic fields. "
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    ABSTRACT: Control of self-assembling systems at the micro- and nano-scale provides new opportunities for the engineering of novel materials in a bottom-up fashion. These systems have several challenges associated with control including high-dimensional and stochastic nonlinear dynamics, limited sensors for real-time measurements, limited actuation for control, and kinetic trapping of the system in undesirable configurations. Three main strategies for addressing these challenges are described, which include particle design (active self-assembly), open-loop control, and closed-loop (feedback) control. The strategies are illustrated using a variety of examples such as the design of patchy and Janus particles, the toggling of magnetic fields to induce the crystallization of paramagnetic colloids, and high-throughput crystallization of organic compounds in nanoliter droplets. An outlook of the future research directions and the necessary technological advancements for control of micro- and nano-scale self-assembly is provided.
    Journal of Process Control 12/2014; 27. DOI:10.1016/j.jprocont.2014.10.005 · 2.65 Impact Factor
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    • "Polymorphic transformations lead to variations in physical properties (e.g., shape, solubility, and chemical reactivity) of crystals, and, therefore, can be detrimental to their performance. The problem of controlling polymorphic transformations consists of ensuring consistent production of the desired polymorph in a stochastic environment [38]. The batch polymorphic crystallization of L-glutamic acid is investigated using the kinetic model developed in [39] for polymorphic transformations of metastable α-form and stable β-form L-glutamic acid crystals. "
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    ABSTRACT: Stochastic uncertainties are ubiquitous in complex dynamical systems and can lead to undesired variability of system outputs and, therefore, a notable degradation of closed-loop performance. This paper investigates model predictive control of nonlinear dynamical systems subject to probabilistic parametric uncertainties. A nonlinear model predictive control framework is presented for control of the probability distribution of system states while ensuring the satisfaction of constraints with some desired probability levels. To obtain a computationally tractable formulation for real control applications, polynomial chaos expansions are utilized to propagate the probabilistic parametric uncertainties through the system model. The paper considers individual probabilistic constraints, which are converted explicitly into convex second-order cone constraints for a general class of probability distributions. An algorithm is presented for receding horizon implementation of the finite-horizon stochastic optimal control problem. The capability of the stochastic model predictive control approach in terms of shaping the probability distribution of system states and fulfilling state constraints in a stochastic setting is demonstrated for optimal control of polymorphic transformation in batch crystallization.
    2014 American Control Conference - ACC 2014; 06/2014
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    ABSTRACT: In this work, different process analytical technologies based on vibrational spectroscopy, i.e., attenuated total reflectance Fourier transform infrared (ATR-FTIR) and Raman spectroscopy, were applied by means of multivariate data analysis techniques. Wide applicability has been demonstrated by in situ monitoring of various crystallization processes, e.g., solubility curve measurement, cooling crystallization, and solvent-mediated polymorph transformation. A calibration strategy has been proposed to obtain accurate and robust estimations of the solute concentration by ATR-FTIR monitoring. Different calibration models and preprocessing techniques were applied and compared. It was shown that these methods allow for solute concentration monitoring of nonisothermal processes even for sparingly soluble substances such as l-glutamic acid in an aqueous environment. An extensive study has been performed to identify the underlying process parameters that influence the Raman signal, i.e., solid composition, solute concentration, suspension density, particle size and shape, and temperature. It is demonstrated that principal component analysis provides qualitative information for seeded and unseeded polymorphic transformations and enables end-point determination of a solid-state transformation process using l-glutamic acid. The multivariate calibration approach described in this work allows for quantitative application of Raman spectroscopy to a multiphase multicomponent dynamic process such as a solvent-mediated polymorphic transformation. Additionally, it was shown that multivariate analysis of Raman data allows for solute concentration estimation despite the fact that solute signals are weak and completely overlapping with signals related to the solid phase.
    Industrial & Engineering Chemistry Research 06/2008; 47(14). DOI:10.1021/ie800236v · 2.59 Impact Factor
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