Design and Optimization of a Combined Cooling/Antisolvent Crystallization Process

Abstract

Design and optimization are important steps during the development of crystallization processes. The combined cooling/antisolvent crystallization of acetylsalicylic acid (ASA) in ethanol−water mixtures is studied by means of experiments and population balance modeling. Model-based approaches require accurate kinetics and thermodynamic data, which are obtained in this work using in situ process monitoring techniques such as attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and focused beam reflectance measurement (FBRM). Solubility is measured in situ as a function of temperature and solvent composition using a multivariate calibration model for the ATR-FTIR. Nucleation and growth kinetics are determined based on crystallization experiments by a combination of a population balance model and an integral parameter estimation technique. The model is finally used to calculate optimal cooling and antisolvent addition profiles of the combined cooling/antisolvent crystallization process using a multiobjective optimization approach which optimizes the process with respect to product properties, that is, particle size distribution, and performance, that is, process time. It was found that the solubility exhibits a maximum at 17 wt% water and that the growth rate correlates well with the solubility. No effect of the solvent composition on the nucleation rate could be identified. The optimized trajectories for cooling and antisolvent could greatly reduce the number of nuclei formed as shown through modeling and experiments. The study shows that combining cooling and antisolvent crystallization allows both improving productivity and reducing the formation of fines, and illustrates how process analytical tools and population balance modeling also are effective in crystallization processes where temperature and solvent composition change.

IUPAC-NIST Solubility Data Series. 102. Solubility of Nonsteroidal Anti-inflammatory Drugs (NSAIDs) in Neat Organic Solvents and Organic Solvent Mixtures

“…Except for a few select systems, the majority of the compiled solubility data was measured at 298.15 K. Maia and Giulietti 61 determined the solubility of 2-acetoxybenzoic acid in ethanol (from 276 to 336 K), 2-propanol (from 282 to 330 K), 1,2-propanediol (from 295 to 334 K), and propanone (from 282 to 326 K) as a function of temperature using a dynamic solubility method that recorded the temperature at which the last crystal of aspirin dissolved in the respective solvent. McLoughlin et al 62 reported the solubility of aspirin in ethanol at both 293 and 333 K, while Lindenberg et al 63 performed solubility measurements of aspirin in ethanol at 298, 308, and 323 K. The authors compared the experimental values determined using an in situ ATR-FTIR spectroscopic method to measured values based on a gravimetric method. The compiled solubility data were correlated with the Abraham solvation parameter model.…”

Phenacetin Crystallization: Cooling Regimes and Crystal Morphology

“…Cyclic temperature cooling programmes may be preferable to linear temperature cooling programs for the size uniformity of crystals (Lindenberg et al, 2009). Single crystal analysis and microscopy can be used to follow the direction of the growth of seed crystals during a crystallization process.…”

Measurement, modelling, and closed-loop control of crystal shape distribution: Literature review and future perspectives

“…Costa et al [164,165] considered a procedure to minimize standard deviation of the final CSD using the successive quadratic progeamming coupled with the discretisation of the control variables, and also the generic algorithm coupled with parameterization of the control variables. Mazzotti et al [166,167] used model-based optimization techniques to control CSD in batch cooling crystallisation of paracetamol in ethanol and also the combined cooling/antisolvent crystallisation of acetylsalicylic acid in ethanol-water mixtures. Rohani and co-workers (see for example [168][169][170][171][172][173][174]) minimized the optimization objective function with respect to a parameter vector temperature input, subject to the mass balance dynamics as well as the PB equation to obtain optimal cooling policy for product quality control.…”