Integrated batch-to-batch and nonlinear model predictive control for polymorphic transformation in pharmaceutical crystallization

Abstract

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-to-batch 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 α-form to the stable β-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. © 2010 American Institute of Chemical Engineers (AIChE).