Deeper process understanding and large cost savings through CFD

In 2017 Delvigne et al. [1] reported in their paper that the use of computational fluid dynamics (CFD) can reduce the number of scale-up trials by 80% with cost savings per trial from $40k to $80k. V. Atiemo-Obeng and S. Kresta, E. Paul reported in their Book “Handbook of Industrial Mixing: Science and Practice” [2], that “for each individual process test, the cost savings from using CFD were estimated to be between $500k –$1 million.”

These are only two examples that report the benefit of using CFD for scale-up and tech transfer in stirred reactors. The present use case shows one specific application of CFD and how it can help gaining deeper insights of the process and deriving conclusions for an informed decision making in the context of scale-up and tech transfer.

Problem statement

In many cases steady-state, single-phase CFD modelling is sufficient for reactor scaling.

However, multi-phase modelling is required when the liquid-gas interface plays an important role for the scale-up. This is the case when the anti-solvent addition position is a crucial parameter due to meso-mixing times close to the threshold where uncontrolled nucleation occurs.

In such a case, choosing the correct addition position and flowrate can make the difference between success and failure. A transient simulation with a fully resolved liquid-gas interface, allows identifying the positions with the shortest mixing times spatially and temporally, ensuring fast mixing to prevent local supersaturation and hence uncontrolled nucleation.

Simulation insights and recommendations

Observations of transient effects in animation:

  • Middle: Regions of shortest mesomixing time are transient – they change in position over time.
  • Middle: Long mesomixing times at the wall (red).
  • Middle: Shortest mesomixing times towards the centre (blue).
  • Right and Left: Upward pointing axial velocity UZ (red) close to the wall.

Recommended actions:

  • Determine nucleation threshold in the lab.
  • Simulate the lab conditions with CFD and extract the corresponding mesomixing time.
  • Simulate large scale reactor and set mesomixing time well above determined threshold.

Instruct process operators:

  • Avoid adding antisolvent at the wall.
  • Follow the findings derived through CFD.
  • If necessary, setup a new antisolvent addition piping system to follow the addition point recommendations and the local addition rate.

Simulation Metrics

The simulation is performed with OpenFOAM v9:

  • Solver: interFoam.
  • Turbulence model: kEpsilon.
  • Impeller rotation model: sliding mesh.
  • Gas-liquid system: Air-water.
  • Mesh size: ~ 1.2Mio.

References

  1. Frank Delvigne, Ralf Takors, Rob Mudde, Walter van Gulik and Henk Noorman, TERRA Research Center, Microbial Processes and Interactions (MiPI), University of Liege, Li ege, Belgium. Institute of Biochemical Engineering, University of Stuttgart, Stuttgart, Germany 2017
  2. V. Atiemo-Obeng, S. Kresta, E. Paul. Handbook of Industrial Mixing Science and Practice (Wiley, Hoboken, NJ, 2004)
  3. Bernd Schmidt,* Jeegna Patel, Francois X. Ricard, Clemens M. Brechtelsbauer, and Norman Lewis GlaxoSmithKline Pharmaceuticals, Old Powder Mills, Tonbridge, Kent, United Kingdom, Organic Process Research & Development 2004: Application of Process Modelling Tools in the Scale-Up of Pharmaceutical Crystallisation Processes
  4. Barrett, Mark, Grady, Des, Casey, Eoin, Glennon, Brian, 2011, 10.1016/j.ces.2011.02.042 JO – Chemical Engineering Science: The role of meso-mixing in anti-solvent crystallization processes