Predictive multiscale computational modeling of nanoparticles in flame reactors

Thumbnail Image
Date
2012-01-01
Authors
Mehta, Maulik
Major Professor
Advisor
Rodney O. Fox
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Organizational Unit
Chemical and Biological Engineering

The function of the Department of Chemical and Biological Engineering has been to prepare students for the study and application of chemistry in industry. This focus has included preparation for employment in various industries as well as the development, design, and operation of equipment and processes within industry.Through the CBE Department, Iowa State University is nationally recognized for its initiatives in bioinformatics, biomaterials, bioproducts, metabolic/tissue engineering, multiphase computational fluid dynamics, advanced polymeric materials and nanostructured materials.

History
The Department of Chemical Engineering was founded in 1913 under the Department of Physics and Illuminating Engineering. From 1915 to 1931 it was jointly administered by the Divisions of Industrial Science and Engineering, and from 1931 onward it has been under the Division/College of Engineering. In 1928 it merged with Mining Engineering, and from 1973–1979 it merged with Nuclear Engineering. It became Chemical and Biological Engineering in 2005.

Dates of Existence
1913 - present

Historical Names

  • Department of Chemical Engineering (1913–1928)
  • Department of Chemical and Mining Engineering (1928–1957)
  • Department of Chemical Engineering (1957–1973, 1979–2005)
    • Department of Chemical and Biological Engineering (2005–present)

    Related Units

Journal Issue
Is Version Of
Versions
Series
Abstract

This dissertation details a predictive computational approach for modeling titanium dioxide

nanoparticles in flame reactors. The industrial production of these nanoparticles is done using

the chloride process, i.e. titanium tetrachloride (TiCl4) is oxidized in a flame to form titanium dioxide (TiO2) particles:

TiCl4(g) + O2(g) &rarr TiO2(s) + 2Cl2(g).

In absence of thermochemical data most previous works used the one-step reaction mechanism given above. But this problem was alleviated recently by West et al. (2009) [R.H. West, R.A. Shirley, M. Kraft, C.F. Goldsmith, W.H. Green, Combust. Flame 156(2009) 1764-1770], by proposing a detailed mechanism for this oxidation process, which includes 30 species and 66 reactions. As the oxidation of TiCl4 happens in a flame, this detailed mechanism becomes more complex with interactions of the hydrocarbons with oxidizer as well as chlorine. Hence, the proposed detailed mechanism in this work extends to 107 species and 501 reactions. Comparisons are made between the one-step and detailed mechanism to show that different models would result in very different product properties.

A bivariate population balance model was proposed to evaluate the size distribution of nanoparticles in the flame reactor. This model tracks both the area and volume distributions and accounts for nucleation, surface growth, aggregation and sintering of the nanoparticles. The results from this model are used to evaluate the particle size and shape for the two chemical mechanisms, which in turn are compared to experimental results. Also explored are the roles of gas-phase and surface phase reactions.

Accurate models for the nanoparticles involve developing a detailed chemical mechanism and modeling the transport process. This is especially true in the case of flame reactors where the flow structure and turbulence are of major importance. Computational fluid dynamics based techniques can be used to understand and implement this coupling between transport processes and chemical reactions. But due to the large number of species and reactions involved, coupling this detailed chemistry with flow solvers is computationally very expensive. Thus, to represent the correct chemistry while making the problem computationally viable reduction of the detailed mechanism is carried out.

Finally, discussed are the results from the successful application of the models and techniques refined during the dissertation work to an industrial system. The findings show that the developed models can accurately track particle evolution in an industrial reactor. In summary, this work uses detailed chemistry and bivariate distribution to present a predictive multiphysics computational model for TiO2 nanoparticle synthesis in flame reactors, that can be employed to optimize operating conditions based on desired product particle size distribution.

Comments
Description
Keywords
Citation
Source
Subject Categories
Copyright
Sun Jan 01 00:00:00 UTC 2012