Models, crystallization process solid-liquid separation

In this section, a discussion of model identification for batch crystallizers is given and a model-based control strategy is illustrated that conveniently handles input, output, and final-time constraints and is applicable to cases in which fines destruction is used and the growth rate is size dependent. This control scheme permits flexibility in objective function formulation and allows consideration of objective functions that take into account solid-liquid separation in subsequent processing steps. 

In Section 11.4, it was shown how suitable solvents can be selected with the help of powerful predictive thermodynamic models or direct access to the DDB using a sophisticated software package. A similar procedure for the selection of suitable solvents was also realized for other separation processes, such as physical absorption, extraction, solution crystallization, supercritical extraction, and so on. In the case of absorption processes or supercritical extraction instead of a g -model, for example, modified UNIFAC, of course an equation of state such as PSRK or VTPR has to be used. For the separation processes mentioned above instead of azeotropic data or activity coefficients at infinite dilution, now gas solubility data, liquid-liquid equilibrium data, distribution coefficients, solid-liquid equilibrium data or VLE data with supercritical compounds are required and can be accessed from the DDB. 

For example, when we consider the design of specialty chemical, polymer, biological, electronic materials, etc. processes, the separation units are usually described by transport-limited models, rather than the thermodynamically limited models encountered in petrochemical processes (flash drums, plate distillations, plate absorbers, extractions, etc.). Thus, from a design perspective, we need to estimate vapor-liquid-solid equilibria, as well as transport coefficients. Similarly, we need to estimate reaction kinetic models for all kinds of reactors, for example, chemical, polymer, biological, and electronic materials reactors, as well as crystallization kinetics, based on the molecular structures of the components present. Furthermore, it will be necessary to estimate constitutive equations for the complex materials we will encounter in new processes. 

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