Wednesday, January 29, 2014

DynoChem population balance models for crystallization: effect of seed amount on crystal size distribution

Templates that use nucleation and growth kinetics in population balance models have been available in the DynoChem online library for some time.  These are a great alternative to writing all of your own code for this problem in MatLab or Excel, or investing in complex software that is in permanent beta-test mode and 'one up from Fortran'.  On the other hand, our templates give you total control over the form of the rate equations, so they are ideal for research purposes.  And you benefit from the features that power users love, like variable time steps, stiff solvers, flexible data handling in Excel format and so on.

DynoChem provides a general-purpose platform for operation modeling and the same environment can be used for anything from early phase reaction kinetics by process chemists through to late phase solvent swap, filtration and drying by process engineers and beyond that into drug product, dissolution and stability applications.  In the pharmaceutical industry, makers of API find countless opportunities to apply these tools over and over again.

Our population balance models come in various shapes and sizes, depending on what you need to accomplish.  The most rigorous of these divide the distribution into size 'classes', with linear or log-spaced intervals, and calculate the number of crystals in each class during nucleation and growth.  Another variant does the reverse, with breakage and dissolution as API crystals dissolve from a tablet in the USP apparatus (or the stomach).

Knowledge of solubility and measurement of some crystallization profiles (notably solute concentration during crystallization) allow the kinetic parameters to be estimated, using the classical approaches described in Mullin's book and many other places.  Armed with reasonable estimates for these parameters, valuable insights into the CSD may be obtained.

During antisolvent crystallization, composition gradients may exist near the feed point and even this can be predicted efficiently using meso- and micromixing models implemented by our team of fluid mixing experts. In general, equipment characterization completes the picture, with the ability to calculate heat transfer, solids suspension and power per unit volume using simple 'utilities'.

Here we show the beneficial impact of seed addition during a cooling crystallization: more seed (up to maximum 3.2% in this case) suppresses nucleation, eliminates a bimodal size distribution (and filtration problems plus product variability concerns) and leads to smaller sizes and a tight distribution.

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