Numerical simulation of deep-bed water filtration

Żywczyk, Ł.; Moskal, Arkadiusz; Gradoń, Leon

doi:10.1016/j.seppur.2015.10.003

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…Many research efforts in industry and academia have been devoted to the simulation of the time evolution, thus kinetics of processes with several species or entities that play a crucial role in research fields such as (bio)chemical engineering, (bio)chemistry, biology, and pharmacology. These processes are determined by, for instance, chemical reactions (e.g., catalyst synthesis and degradation events), purification operations (e.g., water filtration/purification), diffusion (e.g., the spreading of viruses), and pathologies (e.g., Alzheimer’s disease).…”

Section: Introductionmentioning

confidence: 99%

Gillespie-Driven kinetic Monte Carlo Algorithms to Model Events for Bulk or Solution (Bio)Chemical Systems Containing Elemental and Distributed Species

Trigilio

Marien

Steenberge

et al. 2020

Ind. Eng. Chem. Res.

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Stochastic modeling techniques have emerged as a powerful tool to study the time evolution of processes in many research fields including (bio)chemical engineering and biology. One of the most applied techniques is kinetic Monte Carlo (kMC) modeling according to the stochastic simulation algorithm (SSA) as pioneered by Gillespie, in which MC channels and time steps are discretely sampled from probability distributions. In the last decades, the SSA algorithm, as originally developed for systems with elemental species (e.g., A, B, C, etc.), has been further adapted (i) to also tackle systems with distributed species, therefore, populations and (ii) to enable faster algorithm execution. In the present contribution, we highlight the most important developments, taking bulk/solution polymerization as the reference distributed chemical process. We address SSA principles based on conventional array data structures, common acceleration methods (e.g., τ leaping and the scaling method), and the strength of tree- and matrix-based data structures for detailed storage of molecular information per distribution type and even individual population member. In addition, we report advancement regarding array programming and MC sampling methods complemented by the introduction of the use of higher-order trees and root-finding sampling tools. The contribution gives thus a detailed overview of the available main kMC algorithm steps to study kinetics, irrespective of the specific field of application due to their generic nature.

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Section: Introductionmentioning

confidence: 99%

Gillespie-Driven kinetic Monte Carlo Algorithms to Model Events for Bulk or Solution (Bio)Chemical Systems Containing Elemental and Distributed Species

Trigilio

Marien

Steenberge

et al. 2020

Ind. Eng. Chem. Res.

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“…The phenomenological model of filter media describes the macroscopic distribution characteristics of particles in the porous media during the process of deep bed filtration [6,[29][30][31]. Assuming that the flow inside a UBE is one-dimensional and linear, and the dispersion effect of the porous media is negligible.…”

Section: Two-dimensional Particle Mass Conservation Equationmentioning

confidence: 99%

Experimental and Numerical Analysis of Particle Migration and Patterning Behavior in a Gravel Pack

Luo¹,

Xiong²,

Li³

et al. 2023

Fluid Dynamics &Amp; Materials Processing

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Due to its long lifespan and high sand-removal efficiency, gravel packing is one of the most applied sand control methods during the recovery of reservoirs with sanding problems. The blockage and retention of injected sand in a gravel pack is a complex process affected by multiple mechanisms. The majority of existing studies based on the phenomenological deep bed filtration (DBF) theory focused on the gravel pack's overall permeability damage and failed to obtain the inner-pore particle distribution pattern. In this work, experiments and simulations were carried out to reveal the particle distribution in a gravel pack during flooding. In particular, through real-time monitoring of particle migration, the penetration depth and distribution pattern of invaded particles with different gravel-sand particle ratios, fluid viscosities and injection rates could be determined. By simplifying each unit bed element (UBE) into a pore-throat structure with four tunnels (two horizontals for discharge and two verticals for sedimentation), a new network simulation method, which combines deep bed filtration with a particle trajectory model, was implemented. Cross comparison of experimental and numerical results demonstrates the validity and accuracy of the model.

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“…The performance of monolayer and multilayer filters was compared. It showed that, depending on the number of layers, their porosities, sequence of each layer aligned in the structure, and the channel diameter distribution significantly influence the number of particles collected by a particular design [ŻYWCZYK et al 2015].…”

Section: Introductionmentioning

confidence: 99%

Journal of Water and Land Development

2022

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The objective of the present work was to evaluate the hydrodynamic behaviour of a stratified bed filtration column consisting of 4 cm of sand and 2 cm of limestone to remove turbidity and measuring the head loss through the filter in several runs. In this study, two types of sand were used as filtering bed material, one fine and one medium. Crushed limestone was also available. These materials were characterized to determine the average particle diameter, porosity, and permeability coefficient. These were respectively 1.7•10 -4 m, 336.96 and 0.68 m•day -1 for fine sand, 3.3•10 -4 m, 654.24 and 2.59 m•day -1 for the medium sand and 1.26•10 -3 m, 388.8 and 8.64 m•day -1 for crushed limestone. Using these materials, hydrodynamic analyses were carried out using clean water under rapid filtration conditions. In these analyses, different filtration rates were determined to be used in each experiment. Once the filtration rates were determined, the filtration analysis was performed with synthetic turbid water prepared at 8 NTU using tap water and bentonite. From the results obtained, a predictive model was developed based on total head losses for the evaluated filter, maintaining the rapid filtration condition. As a result, a turbidity removal efficiency of 97.7% was obtained with a total head loss of 17.8 cm at a filtration rate of 153 m•day -1 . The developed model predicted head loss as a function of operating time, filtration rate, and filter depth to maximise turbidity removal. The model showed excellent prediction accuracy with R 2 of 0.9999, which indicates that the model predictions are not biased. It was concluded that, due to the porosity of these materials, a stratified bed of sedimentary rocks has a great potential to be used in surface water filtration processes, which implies that it could be used at the rural community level as a form of water treatment, since the material is a readily available, maintenance is simple and low cost, and installation and operation are effortless.

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Numerical simulation of deep-bed water filtration

Cited by 11 publications

References 18 publications

Gillespie-Driven kinetic Monte Carlo Algorithms to Model Events for Bulk or Solution (Bio)Chemical Systems Containing Elemental and Distributed Species

Gillespie-Driven kinetic Monte Carlo Algorithms to Model Events for Bulk or Solution (Bio)Chemical Systems Containing Elemental and Distributed Species

Experimental and Numerical Analysis of Particle Migration and Patterning Behavior in a Gravel Pack

Journal of Water and Land Development

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