SwePub - search: WFRF:(Hounslow M. J.)

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1.	Yu, X., et al. (author) A compartmental CFD-PBM model of high shear wet granulation 2017 In: AICHE Journal. - : Wiley. - 1547-5905 .- 0001-1541. ; 63:2, s. 438-458 Journal article (peer-reviewed)abstract © 2016 American Institute of Chemical EngineersThe conventional, geometrically lumped description of the physical processes inside a high shear granulator is not reliable for process design and scale-up. In this study, a compartmental Population Balance Model (PBM) with spatial dependence is developed and validated in two lab-scale high shear granulation processes using a 1.9L MiPro granulator and 4L DIOSNA granulator. The compartmental structure is built using a heuristic approach based on computational fluid dynamics (CFD) analysis, which includes the overall flow pattern, velocity and solids concentration. The constant volume Monte Carlo approach is implemented to solve the multi-compartment population balance equations. Different spatial dependent mechanisms are included in the compartmental PBM to describe granule growth. It is concluded that for both cases (low and high liquid content), the adjustment of parameters (e.g. layering, coalescence and breakage rate) can provide a quantitative prediction of the granulation process. © 2016 American Institute of Chemical Engineers AIChE J, 63: 438–458, 2017.

Yu, X., et al. (author)
A compartmental CFD-PBM model of high shear wet granulation
2017
In: AICHE Journal. - : Wiley. - 1547-5905 .- 0001-1541. ; 63:2, s. 438-458
Journal article (peer-reviewed)abstract
- © 2016 American Institute of Chemical EngineersThe conventional, geometrically lumped description of the physical processes inside a high shear granulator is not reliable for process design and scale-up. In this study, a compartmental Population Balance Model (PBM) with spatial dependence is developed and validated in two lab-scale high shear granulation processes using a 1.9L MiPro granulator and 4L DIOSNA granulator. The compartmental structure is built using a heuristic approach based on computational fluid dynamics (CFD) analysis, which includes the overall flow pattern, velocity and solids concentration. The constant volume Monte Carlo approach is implemented to solve the multi-compartment population balance equations. Different spatial dependent mechanisms are included in the compartmental PBM to describe granule growth. It is concluded that for both cases (low and high liquid content), the adjustment of parameters (e.g. layering, coalescence and breakage rate) can provide a quantitative prediction of the granulation process. © 2016 American Institute of Chemical Engineers AIChE J, 63: 438–458, 2017.

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