Population balance model based multi-objective optimization and robustness analysis of a continuous plug flow antisolvent crystallizer

Abstract

Crystallization is a major separation process in the pharmaceutical industry. Most crystallizations are performed batchwise, but there is great incentive for switching to continuous operation. We have investigated the modeling, simulation, optimization, and robustness of a multi-segmented, multi-addition plug-flow crystallizer (MSMA-PFC). The design accepts multiple antisolvent flows along its length, permitting localized control of supersaturation. A mass balance equation was used to track the depletion of dissolved solute (flufenamic acid), and a population balance equation for tracking the crystal size distribution. Multiobjective optimization was done using the antisolvent flowrates into each segment as decision variables. The genetic algorithm was used to calculate the Pareto frontiers for the two competing objectives of maximizing average crystal size (L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">43</inf> ), and minimizing coefficient of variation (CV). The sensitivity of the Pareto frontier to variation in the growth and nucleation kinetic parameters was investigated. The robustness of a single solution was examined as well with respect to error in the kinetic parameters, as well as to errors in antisolvent flowrate.