Seeded Batch Crystallizations

Although in some cases heterogeneous seeding can be applied, the seeds in this chapter are understood as crystalline material of the solute to be crystallized. These are usually in milled form, both with respect to chemical purity and with respect to the solid-state form (polymorphism or solvate form). 

Seeding a batch crystallization and thus avoiding spontaneous nudeation gives the possibility to achieve a number of important goals and at the same time makes it more robust  

Particle shape can be manipulated if adequate seeds are used. If needle-hke crystals are micronized to a compact shape and these particles are used in a large amount (e.g., 10% of the expected yields), the resulting product crystals are forced to have an aspect ratio of less than 10, even if they would grow only in the longitudinal direction. 

In every case, seeding should be applied in a way to take away uimecessary degrees of freedom for the crystallization process and obtain the desired product in a reproducible way. 

Usually it is very helpful if seed crystals are very well defined and specified - that is, milled to the desired PSD. In other cases - if former product crystals are used as seeds - in the long run the product particle size will become larger and larger, efficient seed surface area will accordingly become smaller, and the process will change and no longer deliver the desired product quality. 

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Several features of secondary nucleation make it more important than primary nucleation in industrial crystallizers. First, continuous crystallizers and seeded batch crystallizers have crystals in the magma that can participate in secondary nucleation mechanisms. Second, the requirements for the mechanisms of secondary nucleation to be operative are fulfilled easily in most industrial crystallizers. Finally, low supersaturation can support secondary nucleation but not primary nucleation, and most crystallizers are operated in a low supersaturation regime that improves yield and enhances product purity and crystal morphology. 

A theoretical analysis of an idealized seeded batch crystallization by McCabe (1929a) lead to what is now known as the AL law . The analysis was based on the following assumptions (a) all crystals have the same shape (b) they grown invariantly, i.e. the growth rate is independent of crystal size (c) supersaturation is constant throughout the crystallizer (d) no nucleation occurs (e) no size classification occurs and (f) the relative velocity between crystals and liquor remains constant. 

Chianese, A., Di Berardino, F. and Jones, A.G., 1993. On the effect of crystal breakage on the fine crystal distribution from a seeded batch crystallizer. Chemical Engineering Science, 48, 551-560. 

Girolami, M.W. and Rousseau, R.W., 1985. Initial breeding in seeded batch crystallizers. Industrial and Engineering Chemistry Research, 25, 66-70. 

The Investigation was carried out using a seeded, batch crystallization In the absence of nucleatlon. Supersaturated solutions were prepared, seeded and maintained at a constant temperature while crystallization proceeded. Samples were taken periodically to give a solution for analysis and crystals for size analysis and crystal content determination. 

The above equations can be applied to any batch crystallization process, regardless of the mode by which supersaturation is generated. For example, suppose a model is needed to guide the operation of a seeded batch crystallizer so that solvent is evaporated at a rate that gives... 

Fig. 7 Schematic of the metastable zone, which is the region surrounded by the solubility curve and the metastable limit. This is the operating region for most seeded batch crystallization.

Seeded batch crystallization process Three problems with two or three objectives from (1) maximization of the weight mean size of the crystal size disuibution, (2) minimization of the nucleated product, (3) minimization of total time of operation, and (4) minimization of coefficient of variation. NSGA-n Dynamic optimization problems were solved to find the optimal temperature profile. Sarkar et al. (2006)... 

Sarkar, D., Rohani, S. and Jutan, A. (2006). Multi-objective optimization of seeded batch crystallization processes, Chem. Eng. Sci., 61, pp. 5282-5295. 

Cumulative CSD Method. Misra and White (1971) used a cumulative CSD method to study the crystallization kinetics of aluminum trihydroxide from a seeded batch crystallizer. The population balance equation corresponding to Eq. (10.3) can be written as... 

Figure 9.13 demonstrates the benefits of employing programme-controlled cooling during a seeded batch crystallization of potassium sulphate in which... 

The systematic gypsum scale fonnation and control study was carried out in four stages of experiments, namely seeded batch crystallization, seeded continuous crystallization, single pipe flow, and multiple pipe flow, respectively. For all the experiments, the supersaturated gypsum scale forming solutions were prepared by dissolving in distilled water at room... 

Determination of the optimal temperature (or supersaturation) trajectory for a seeded batch crystallizer is a well studied problem. This is a dynamic optimization or optimal control problem. The process performance is determined by the crystal size distribution and product yield at the final time. For uniformity of shape and size in the crystals in a seeded batch crystallization process, it is essential to ensure that the nucleation phenomena occurs to the minimum and mostly the seeded crystals grow to the desired size at a certain rate. If nucleation occurs in the initial phase, then there is a possibility that the nucleated crystal will compete with the seeded ones, thus if the phenomena is of late growth, then nucleation in the earlier phase is preferred. Thus, depending upon the process operation, many types of objective functions have been proposed [4]. 

J. Ward, D. MeUichamp, and M. Doherty. Choosing an operating policy for seeded batch crystallization. AIChE J., 52(6) 2046-2054, 2006. 

K. Yenkie and U. Diwekar. Stochastic optimal control of seeded batch crystallizer applying the ito process. AIChE J., 51(ll) 3000-3006, 2005. 

Q. Hu, S. Rohani, and A. Jutan. Modelling and optimization of seeded batch crystallizers. Computers and Chemical Engg, 29 911-918, 2005. 

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