Robust optimal control of polymorphic transformation in batch crystallization

AIChE Journal (Impact Factor: 2.58). 08/2007; 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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    ABSTRACT: This review discusses important research developments and arising challenges in the field of industrial crystallization with an emphasis on recent problems. The most relevant areas of research have been identified. These are the prediction of phase diagrams; the prediction of effects of impurities and additives; the design of fluid dynamics; the process control with process analytical technologies (PAT) tools; the polymorph and solvate screening; the stabilization of non-stable phases; and the product design. The potential of industrial crystallization in various areas is outlined and discussed with particular reference to the product quality, process design, and control. On this basis, possible future directions for research and development have been pointed out to highlight the importance of crystallization as an outstanding technique for separation, purification as well as for product design.
    Frontiers of Chemical Science and Engineering. 7(1).
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    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 α-form to the stable β-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. © 2009 American Institute of Chemical Engineers AIChE J, 2009
    AIChE Journal 08/2009; 55(10):2631 - 2645. · 2.58 Impact Factor
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    ABSTRACT: Most pharmaceutical manufacturing processes include a series of crystallization processes to increase purity with the last crystallization used to produce crystals of desired size, shape, and crystal form. The fact that different crystal forms (known as polymorphs) can have vastly different characteristics has motivated efforts to understand, simulate, and control polymorphic crystallization processes. This article proposes the use of weighted essentially nonoscillatory (WENO) methods for the numerical simulation of population balance models (PBMs) for crystallization processes, which provide much higher order accuracy than previously considered methods for simulating PBMs, and also excellent accuracy for sharp or discontinuous distributions. Three different WENO methods are shown to provide substantial reductions in numerical diffusion or dispersion compared with the other finite difference and finite volume methods described in the literature for solving PBMs, in an application to the polymorphic crystallization of L-glutamic acid. © 2008 American Institute of Chemical Engineers AIChE J, 2009
    AIChE Journal 12/2008; 55(1):122 - 131. · 2.58 Impact Factor

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