in Wiley InterScience (www.interscience.wiley.com).Polymorphism, in which there exist different crystal forms for the same chemical compound, is an important phenomenon in pharmaceutical manufacturing. In this article, a kinetic model for the crystallization of L-glutamic acid polymorphs is developed from experimental data. This model appears to be the first to include all of the transformation kinetic parameters including dependence on the temperature. The kinetic parameters are estimated by Bayesian inference from batch data collected from two in situ measurements: ATR-FTIR spectroscopy is used to infer the solute concentration, and FBRM that provides crystal size information. Probability distributions of the estimated parameters in addition to their point estimates are obtained by Markov Chain Monte Carlo simulation. The kinetic model can be used to better understand the effects of operating conditions on crystal quality, and the probability distributions can be used to assess the accuracy of model predictions and incorporated into robust control strategies for polymorphic crystallization.

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

Polymorphism, a phenomenon in which a substance can have more than one crystal form, is a frequently encountered phenomenon in pharmaceutical compounds. Different polymorphs can have very different physical properties such as crystal shape, solubility, hardness, color, melting point, and chemical reactivity, so that it is important to ensure consistent production of the desired polymorph. In this study, an integrated batch-tobatch and nonlinear model predictive control (B2B-NMPC) strategy based on a hybrid model is developed for the polymorphic transformation of L-glutamic acid from the metastable a-form to the stable b-form crystals. The hybrid model comprising of a nominal first-principles model and a correction factor based on an updated PLS model is used to predict the process variables and final product quality. At each sampling instance during a batch, extended predictive self-adaptive control (EPSAC) is employed as a NMPC technique to calculate the control action by using the current hybrid model as a predictor. At the end of the batch, the PLS model is updated by utilizing the measurements from the batch and the above procedure is repeated to obtain new control actions for the next batch. In a simulation study using a previously reported model for a polymorphic crystallization with experimentally determined parameters, the proposed B2B-NMPC control strategy produces better performance, where it satisfies all the state constraints and produces faster and smoother convergence, than the standard batch-to-batch strategy.

in Wiley InterScience (www.interscience.wiley.com).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.

in Wiley InterScience (www.interscience.wiley.com).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 a-form to the stable b-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