Dataset Information

Predicting the performance of pressure filtration processes by coupling computational fluid dynamics and discrete element methods.

ABSTRACT: 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 solvent flow through the solid particle cake. The model was validated using data collected by filtering mixtures of spherical glass beads and deionized water through a dead-end cell over a range of applied pressures. Numerical experiments were performed to study the effects of particle properties, liquid properties and operating conditions on filtration performance. The model predicted that the filtrate flow rate could be strongly affected by the mean size of the 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. Our study demonstrated that CFD-DEM modeling is a powerful approach for understanding cake filtration processes and predicting filtration performance.

SUBMITTER: Li B

PROVIDER: S-EPMC6774648 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting the performance of pressure filtration processes by coupling computational fluid dynamics and discrete element methods.

Li Boyang B Dobosz Kerianne M KM Zhang Haitao H Schiffman Jessica D JD Saranteas Kostas K Henson Michael A MA

Chemical engineering science 20190813

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 solvent flow through the solid particle cake. The model was validated using data collected by filtering mixtures of spherical glass beads and deionized water through a dead-end cell over a range of applied pressures. Numerical experiments were performed to study the ef ...[more]

PMID: 31579324

Similar Datasets

Project description:Understanding and controlling biological function of proteins at the atomic level is of great importance; allosteric mechanisms provide such an interface. Experimental and computational methods have been developed to search for residue mutations that produce changes in function by altering sites of correlated motion. These methods are often observational in that altered motions are achieved by random sampling without revealing the underlying mechanism(s). We present two deterministic methods founded on structure-function relationships that predict dynamic control sites (i.e., locations that experience correlated motions as a result of altered dynamics). The first method ("static") is based on a single structure conformation (e.g., the wild type (WT)) and utilizes a graph description of atomic connectivity. The local atomic interactions are used to compute the propagation of contact paths. This description of structure connectivity reveals flexible locations that are susceptible to altered dynamics. The second method ("dynamic") is a comparative analysis between the normal modes of a WT structure and a mutant structure. A mapping function is defined that quantifies the significance of the motions in one structure projected onto the motions of the other. Each mode is considered up- or down-regulated according to its change in relative significance. This description of altered dynamics is the basis for a motion correlation analysis, from which the dynamic control sites are readily identified. The methods are theoretically derived and applied using the canonical system dihydrofolate reductase (DHFR). Both methods demonstrate a very high predictive value (p<0.005) in identifying known dynamic control sites. The dynamic method also produces a new hypothesis regarding the mechanism by which the DHFR mutant achieves hyperactivity. These tools are suitable for allosteric investigations and may greatly enhance the speed and effectiveness of other computational and experimental methods.

Dataset Information

Predicting the performance of pressure filtration processes by coupling computational fluid dynamics and discrete element methods.

Publications

Predicting the performance of pressure filtration processes by coupling computational fluid dynamics and discrete element methods.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure