Granulation processes, modeling coalescence

Extent of Noninertial Growth Growth by coalescence in granulation processes may be modeled by the population balance. (See the... 

In practice, many coalescence kernels are determined empirically and based on laboratory or plant data specific to the granulation process and the product. More recent, physically derived kernels (Litster and Ennis, 2004) have been tested in laboratories and over time are expected to slowly replace empirically derived kernels in industrial models. 

Growth by coalescence in granulation processes may be modeled by population balances (Chapter 21). It is necessary to determine both the mechanism and the kernel which describe growth. For fine powders within the noninertial regime of growth, all collisions result in successful coalescence provided binder is present. Coalescence occurs via a random, size independent kernel which is only a function of liquid loading y, as well as mixing, or... 

Artificial realization of the system behavior (35). This method is commonly applied to complex particulate processes, which are described in some detail here. In the artificial realization, the direct evaluation of integral and differential functions is replaced by the simulation of the stochastic behavior modeled by using a randomness generator to vary the behavior of the system (20). The important probabilistic functions in the original model equations, such as coalescence kernels for granulation processes, are still essential in Monte Carlo simulations and are shown later. 

Monte Carlo methods for the artificial realization of the system behavior can be divided into time-driven and event-driven Monte Carlo simulations. In the former approach, the time interval At is chosen, and the realization of events within this time interval is determined stochastically. Whereas in the latter, the time interval between two events is determined based on the rates of processes. In general, the coalescence rates in granulation processes can be extracted from the coalescence kernel models. The event-driven Monte Carlo can be further divided into constant volume methods... 

Adetayo AA, Ennis BJ. A unifying approach to modelling granulation processes coalescence mechanisms. AIChE J 1997 43(l) 927-934. 

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