(300b) Crystallization Kinetics Identification within a Generic Modeling Framework

Crystallization serves as an efficient separation
process for compounds, which are solid in their pure form at the given
separation conditions. Crystallization is an essential step in the production
of many pharmaceutical products as the active pharmaceutical ingredients (APIs)
are often separated efficiently through this operation. The monitoring and
analysis of crystallization operations has recently received increased
attention due to the growing need to control the final crystal size
distribution (CSD) in a relatively narrow range as well as to measure and monitor
the final product characteristics  CITATION EAa10 \l 1030  (1). This monitoring and
analysis requires models that describe the multiple phenomena and their
interplay encountered in crystallization operations in order to have the
ability to design effective control strategies and the possibility of planning crystallization
operations. To this end, models of the phenomena (kinetics, solubility, etc.) are
needed within a generic framework that allows the generation of the needed models
to achieve a more complete description of specific crystallization operations.
For example, certain size distributions can be desired in order to obtain
specific properties for the product such as rate of dissolution. Growth of the
crystals occurs in multiple dimensions and the growth rates of the facets
determine the shape of the crystals. The size distribution is obtained because
of different competing phenomena such as nucleation and growth, each with different
kinetics. A full representation of a crystallizer requires models for all these
kinetic phenomena and saturation descriptions (constitutive equations) coupled
with descriptions of the equipment and an operational policy. Such a
description is possible within a generic framework where the models can be
combined, reused and identified. This framework has been established CITATION Noo11 \l 1030  (2) and tested for
simulation of selected crystallization processes.

The objective of this work is to provide the
ability to establish the kinetics of a crystallization operation systematically
and efficiently. Initially an operational scenario is defined for which the
specific balance equations are set up. The constitutive models are chosen based
on the system description and the objectives of the model. Once the model is
ready, that is, all model parameters are available, it can be used for
simulation of the corresponding crystallization operations. If an established
model for the desired kinetics is found within the model library, then this
model is used. If not available, or in case a new model is desired, then
identification of model parameters is performed. This identification step requires
measured data, which may be available in different forms such as single crystal
growth data or chord length measurements (for example, data from Focused Beam
Reflectance Measurements, FBRM). Use of such data requires appropriate translational
policies to convert measurements into one or more variables that are described
by the model. A preliminary version has been developed and tested to obtain
information about the development of crystal size distribution (CSD) in a given
operation. The measurement types that can be handled through the modeling framework
are continuously being expanded to accept more data types with information
relevant for the crystallization operations. The measurements can be used for
offline analysis and parameter regression. With an established kinetic model it
is thus possible to translate the predicted model results into a form, which is
directly comparable (and visualized) to the data available for evaluation of
the model. Furthermore, if there is model ? data mismatch, the measured data
can be used for online parameter estimation.

The
expanded model framework combined with the systematic approach to establish the
kinetic models for use in general crystallization operations in combination
with monitoring tools will be presented using case studies involving different
scenarios for crystallization operations.

Works Cited

 BIBLIOGRAPHY 1. Optimal seed recipe design for crystal size distributionn control for batch cooling crystallisation processes. E. Aamir, Z.K. Nagy, C.D.Rielly. 2010, Chemical Engineering Science, Vol. 65, pp. 3602-3614.

2. A generic multi-dimensional model-based system for batch cooling. Noor Asma Fazli Abdul Samad, Ravendra Singh, Gürkan Sin, Krist V. Gernaey, Rafiqul Gani. 2011, Computers and Chemical Engineering, Vol. 35, pp. 828?843.