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Rate Dispersion (rate + dispersion)

Distribution by Scientific Domains
Chemistry100%

Kinds of Rate Dispersion

  • growth rate dispersion
    		•

    Selected Abstracts

    Modeling Growth Rate Dispersion in Industrial Crystallizers

    CHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 3 2003
    G.M. Westhoff
    Abstract The phenomenon of healing appears to be a plausible explanation for the growth rate dispersion observed in many industrial crystallizers. In this paper a growth model is postulated, which describes the healing of plastically deformed attrition fragments. The rate of healing is assumed to be inversely proportional to the initial strain and to the rate of change of either the length, the area, or the volume of the crystal. The validity of the proposed model is verified by the simulation of growth of the smallest crystals (L0) in time in a growth experiment for specific combinations of the model parameters. In addition, the applicability of the proposed model is evaluated through simulations of steady state experimental data obtained in a 75-liter Draft Tube (DT) crystallizer. It is concluded that the proposed model is able to fit reasonably well the experimental crystal size distribution. The model predicts the existence of a ,dead time' during which attrition fragments with large initial strain do not grow and which may last several residence times. [source]

    Modeling the crystallization of proteins and small organic molecules in nanoliter drops

    AICHE JOURNAL, Issue 1 2010
    Richard D. Dombrowski
    Abstract Drop-based crystallization techniques are used to achieve a high degree of control over crystallization conditions in order to grow high-quality protein crystals for X-ray diffraction or to produce organic crystals with well-controlled size distributions. Simultaneous crystal growth and stochastic nucleation makes it difficult to predict the number and size of crystals that will be produced in a drop-based crystallization process. A mathematical model of crystallization in drops is developed using a Monte Carlo method. The model incorporates key phenomena in drop-based crystallization, including stochastic primary nucleation and growth rate dispersion (GRD) and can predict distributions of the number of crystals per drop and full crystal size distributions (CSD). Key dimensionless parameters are identified to quickly screen for crystallization conditions that are expected to yield a high fraction of drops containing one crystal and a narrow CSD. Using literature correlations for the solubilities, growth, and nucleation rates of lactose and lysozyme, the model is able to predict the experimentally observed crystallization behavior over a wide range of conditions. Model-based strategies for use in the design and optimization of a drop-based crystallization process for producing crystals of well-controlled CSD are identified. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [source]

    Crystal growth rate dispersion modeling using morphological population balance

    AICHE JOURNAL, Issue 9 2008
    Cai Y. Ma
    Abstract Crystal growth in solution is a surface-controlled process. The variation of growth rates of different crystal faces is considered to be due to the molecular arrangement in the crystal unit cell as well as the crystal surface structures of different faces. As a result, for some crystals, the growth rate for a specific facet is not only a function of supersaturation, but also dependent on some other factors such as its size and the lattice spread angle. This phenomenon of growth rate dispersion (GRD) or fluctuation has been described in literature to have attributed to the formation of some interesting and sophisticated crystal structures observed in experimental studies. In this article, GRD is introduced to a recently proposed morphological population balance model to simulate the dynamic evolution of crystal size distribution in each face direction for the crystallization of potash alum, a chemical that has been reported to show GRD phenomenon and sophisticated crystal structures. The GRD is modeled as a function of the effective relative supersaturation, which is directly related to crystal size, lattice spread angle, relative supersaturation, and solution temperature. The predicted results clearly demonstrated the significant effect of GRD on the shape evolution of the crystals. © 2008 American Institute of Chemical Engineers AIChE J, 2008 [source]

    Modeling Growth Rate Dispersion in Industrial Crystallizers

    CHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 3 2003
    G.M. Westhoff
    Abstract The phenomenon of healing appears to be a plausible explanation for the growth rate dispersion observed in many industrial crystallizers. In this paper a growth model is postulated, which describes the healing of plastically deformed attrition fragments. The rate of healing is assumed to be inversely proportional to the initial strain and to the rate of change of either the length, the area, or the volume of the crystal. The validity of the proposed model is verified by the simulation of growth of the smallest crystals (L0) in time in a growth experiment for specific combinations of the model parameters. In addition, the applicability of the proposed model is evaluated through simulations of steady state experimental data obtained in a 75-liter Draft Tube (DT) crystallizer. It is concluded that the proposed model is able to fit reasonably well the experimental crystal size distribution. The model predicts the existence of a ,dead time' during which attrition fragments with large initial strain do not grow and which may last several residence times. [source]