Detailed population balance modelling of TiO2 synthesis in an industrial reactor

Authors: Astrid Boje, Jethro Akroyd, Stephen Sutcliffe, John Edwards, and Markus Kraft*

  • An industrial titania reactor is modelled.
  • A multivariate particle description is used to explore particle structure.
  • A reactor network is used to describe local flow, composition, and temperature.
  • A short process parameter study demonstrates potential use as a design tool.

abstractThis paper uses a network of ideal flow reactors and a detailed population balance model to study the evolution of the size and shape distributions of pigmentary titanium dioxide, formed under industrial synthesis conditions. The industrial reactor has multiple reactant injections, a tubular working zone in which the exothermic reaction is completed, and a cooling zone. A network of continuously stirred tank reactors is used to model variation in composition around the feeds and plug flow reactors with prescribed temperature gradients are used to describe the working and cooling zones. The quality of the industrial product depends on its morphology, and this is influenced by factors including temperature and throughput. In this paper, a multivariate particle model is accommodated using a stochastic method and the particle morphology is characterised in terms of the distributions of primary and aggregate particle diameters, number of primary particles per particle and neck radii of connected primary particles. Increasing temperature or residence time is shown to produce larger particles. Qualitative similarities are highlighted between such findings and previous studies. The throughput studies are also in qualitative agreement with empirical industrial experience. There is scope for extending and improving the current model; however, it is suggested that insights of this type could be used to inform the design and operation of the industrial process.

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Keywords: ideal reactor, network, particle model, population balance, stochastic, titanium dioxide,

Associated Projects: Nanoparticles and Particle Processes

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