(133e) Predicting Pressure Filtration Performance By CFD-DEM Coupling Approach

Pressure filtration is an important solid-liquid separation operation in which a particle suspension is subjected to an applied pressure and the liquid is forced through a filter medium that is only permeable to the fluid phase. Common applications include separation of solid particles from a mother liquor in the pharmaceutical industry and recovery of microorganisms from fermentation broths in the biotechnology industry. The porous cakes formed by different particle suspensions can exhibit widely different filtration properties including filtration times that varying from hours to weeks under similar operating conditions. The filtrate flow rate is dependent on operating conditions (e.g. applied pressure), liquid properties (e.g. viscosity) and cake properties (e.g. resistance). Because pressure filtration often is one step in a larger batch processing plant, there is considerable motivation to improve filtration performance by reducing the filtration cycle time. Process modeling represents a powerful tool for developing improved understanding and enhanced efficiency of pressure filtration processes. Although conventional continuum models provide macroscopic predictions such as the time-dependent cake thickness and filtrate volumetric flow rate, this approach is unable to account for the effects of the particle size/shape distribution on cake structure and filtration performance.

To obtain a fundamental understanding of the various factors affecting pressure filtration performance, we developed a coupled computational fluid dynamics (CFD) and discrete element method (DEM) model for simulating the effect of the solid particle characteristics on fluid flow through the cake. The combined CFD-DEM model captured both particle-particle and particle-fluid interactions through fundamental mathematical descriptions. The model was validated using data collected by filtering spherical glass beads and deionized water mixtures using a dead-end cell over a range of applied pressures and for two mean particle sizes. Numerical experiments were then performed to study the effects of particle properties, liquid properties and operating conditions on filtration performance. The model predicted that the cake resistance and filtrate flow rate could be strongly affected by the mean size and polydispersity of the spherical particles, the presence of small particles (i.e. fines) in the particle distribution, the viscosity of the liquid, and particle deformation leading to cake compression. We extended the filtration model to account for non-spherical, polydisperse particles more representative of actual APIs (active pharmaceutical ingredients). Simulations predicted that particle shape can have a strong impact on filtration performance, especially when the shape distribution is strongly polydisperse.