Type of Document Dissertation Author Widenski, David John Author's Email Address email@example.com URN etd-03302012-084257 Title A Thermodynamic Framework for the Modeling and Optimization of Crystallization Processes Degree Doctor of Philosophy (Ph.D.) Department Chemical Engineering Advisory Committee
Advisor Name Title Romagnoli, Jose A. Committee Chair Hung, Francisco R. Committee Member Spivey, James J. Committee Member Thompson, Karsten E. Committee Member Kundu, Sukhamay Dean's Representative Keywords
- Predictive Solubility Models
Date of Defense 2011-10-12 Availability unrestricted AbstractCrystallization is a widely used chemical engineering separation unit operation process. Since this technique can produce high purity products it is used for the industrial production of many chemical compounds, such as pharmaceuticals, agrochemicals, and fine chemicals. The production of these products is a multi-million dollar industry. Any methods to improve the production of these products would be highly valued. Thus, the main objective of this work is to target model-based optimal strategies for crystallization operations specifically targeting crystal size and crystal size distribution (CSD). In particular, take the knowledge gained and translate it into an economically and practically feasible implementation that is utilizable by the pharmaceutical industry.
To achieve this, a comprehensive crystallization modeling framework is developed. This framework predicts the CSD while taking into account temperature, seeding variables, and antisolvent feed rates. In addition, this framework takes into account the recent proliferation of predictive thermodynamic solubility models. These solubility models have the potential to greatly reduce the need for experimental data, thus, improving the crystallization modelís predictive ability. Finally, these crystallization models are implemented into the gPROMS modeling software and are used for model-based optimization.
The crystallization modeling framework is developed for several different scenarios. One framework consists of a full thermodynamic crystallization model for potassium chloride. This modeling framework when combined with model-based optimization is proven to be superior to heuristic methods. Another framework, which utilizes several different predictive thermodynamic solubility models, evaluates their use to predict crystallization behavior and to determine optimal operating conditions, cooling profiles, and antisolvent feed profiles. It is shown that these models can be used to determine optimal operating conditions and cooling profiles, but they are not sufficiently accurate to be used to determine optimal antisolvent feed profiles. The last crystallization framework is developed for the non-isothermal antisolvent crystallization of sodium chloride. This framework shows that for systems whose solute solubility is relatively independent of temperature, adding temperature control as a second degree of freedom is beneficial. In particular, it allows for the production of crystal mean sizes unattainable at other temperatures, and for the joint control of particle mean size and dispersion.
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