Predictive modelling of organic crystallization processes
This thesis is concerned with the development of a predictive model for batch cooling suspension pharmaceutical crystallizations, with a focus on product performance. A major challenge involved in the design of industrial pilot plant pharmaceutical crystallizers, is to predict the influence of crystallizer geometry, scale and operating conditions on the process behaviour and crystal size distribution (CSD). The design of industrial crystallizers is hindered by the lack of scale-up rules due to the absence of reliable predictive process models. Currently no reliable predictive or 'dial up a particle size' tool exists for scale-up predictions. The research involves the development of a novel predictive compartmental modelling framework for the scale-up of an organic fine chemical. A new approach of using compartments is developed in order to facilitate scale-up design and process modelling by separating crystallization kinetic and hydrodynamic phenomena. Application of this technique involves determining key process engineering information on a laboratory scale, which is critical for technology transfer, and combining this data with hydrodynamic information on transfer to large scale for predictive scale-up purposes. The key process engineering information required for predictive modelling includes the determination of solubility characteristics, thermodynamic properties and crystallization kinetics of the organic fine chemical. Attenuated Total Reflectance Ultra-Violet (ATR-UV) spectroscopy is used as an 'in-situ' measurement technique to measure solute concentration. A modified continuous Mixed Suspension Mixed Product Removal (MSMPR) crystallizer is designed specifically for innovative drug candidates available in limited quantities to derive steady state crystallization kinetics with minimal influence from hydrodynamic phenomena. Batch attrition experiments were carried out to determine the effects of specific power input on the CSD using Lasentec Focussed Beam Reflectance Monitoring (FBRM) to monitor the process on-line and to develop an attrition rate model. Computational Fluid Dynamics (CFD) is a simulation tool that is also introduced to provide valuable insight into mixing, heat transfer and hydrodynamic phenomena within agitated batch cooling suspension crystallization vessels including investigating the effects of scale-up. CFD is used to aid the development of the compartmental modelling framework. The design of the compartmental structure is based on high spatial resolution CFD simulations of internal flow, mixing and heat transfer within crystallizers upon scale-up. The great advantage of using a compartmental modelling framework is that the spatial resolution is reduced and the full population balance with kinetic models can be implemented. The detailed compartmental framework is based on the overall flow pattern, local energy dissipation rate, solids concentration and temperature distribution obtained from CFD. The number, location, cross-sectional area and volume of compartments are determined from CFD results based on the physical crystallizer dimensions. The compartments are selected such that they have approximately uniform temperature, local energy dissipation and solids concentration. Each dynamic compartment has a mass, concentration, enthalpy and population balance combined with MSMPR crystallization kinetic models. The compartments are therefore well mixed and physically connected via interconnecting flows determined from CFD. A general process modelling tool, gPROMS (Process Systems Enterprises) that supports both steady state and dynamics simulations is used to solve sets of ordinary differential and algebraic equations in each compartment. A single compartmental modelling approach is used initially as a first approach without taking into account local variations in process conditions. Predictions on a laboratory scale for an MSMPR and batch cooling crystallizer were satisfactory but upon scale-up the effects of mixing and hydrodynamics is not taken into account and therefore the predictions become less reliable. A compartmentalization approach can be introduced into gPROMS whereby the compartments are modelled as individual units with input and output streams using CFD hydrodynamic information.