Experimental Study and Computational Fluid Dynamics Simulation of a Full-Scale Membrane Bioreactor for Municipal Wastewater Treatment Application

Amini, Ershad; Mehrnia, Mohammad Reza; Mousavi, Seyyed Mohammad; Mostoufi, Navid

doi:10.1021/ie400632y

Cited by 38 publications

(21 citation statements)

References 25 publications

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“…Examination was focused on the near-membrane region. Water velocity measured along the line of: x = [À20, 20] mm, y = 120 mm, and z = 15 mm showed good agreement with that simulated by CFD with an average error of 1.4%, which was much smaller than literature report (8-30%) (Amini et al, 2013;Ratkovich et al, 2012). This proved that the turbulence and multiphase models were chosen appropriately in the CFX package, and the meshes generated had satisfactory quality to give a reliable simulation of realistic fluid dynamics in this study.…”

Section: Experimental Validation Of Cfd Simulationsupporting

confidence: 58%

Hydraulic optimization of membrane bioreactor via baffle modification using computational fluid dynamics

Yan

Xiao

Liang

et al. 2015

Bioresource Technology

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Section: Experimental Validation Of Cfd Simulationsupporting

confidence: 58%

Hydraulic optimization of membrane bioreactor via baffle modification using computational fluid dynamics

Yan

Xiao

Liang

et al. 2015

Bioresource Technology

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“…The interphase force

{true \vec{R}}_{j i} = - {true \vec{R}}_{i j}

is defined as:

\sum_{j = 1}^{n} {\overset{R}{true}}_{j i} = \sum_{i = 1}^{n} K_{j i} true({true \vec{v}}_{i} - {true \vec{v}}_{j} true)

where K ji = K ij is the interphase momentum exchange coefficient, which can be written as follows:

K_{j i} = \frac{α_{i} α_{j} ρ_{j} f}{τ_{j}}

where f and τ j are drag function and particulate relaxation time, respectively:

f = \frac{C_{D} Re}{24}

τ_{j} = \frac{ρ_{j} {d_{j}}^{2}}{18 μ_{i}}

where C D is the drag coefficient and Re is the Reynolds number:

C_{D} = true{\begin{matrix} left \\ center \frac{24 true(1 + 0.15 {Re}^{0.687} true)}{normalRe} \begin{array}{c} center \end{array} Re \leq 1000 \\ center 0.44 \begin{array}{c} center & \begin{array}{c} center & \begin{array}{c} center & \begin{array}{c} center \end{array} \end{array} \end{array} \end{array} \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

“…where I, m i , and l i are the unit tensor, the shear, and bulk viscosity of phase i, respectively. The interphase forceR ji ¼ ÀR ij is defined as: [35] X n j¼1R ji ¼ where K ji ¼ K ij is the interphase momentum exchange coefficient, which can be written as follows: [35]…”

Section: Eulerian Multiphase Modelmentioning

confidence: 99%

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Experiments and a three‐phase computational fluid dynamics (CFD) simulation coupled with population balance equations of a stirred tank bioreactor for high cell density cultivation

et al. 2015

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This research describes a three dimensional (3‐D) computational fluid dynamics (CFD) simulation coupled with population balance equations (PBE) to study hydrodynamics and biomass production in a laboratory‐scale stirred‐tank bioreactor. The gas‐liquid‐solid flow was modelled using a Eulerian multiphase and k−ϵ turbulence (RNG) model. The energy dissipation rates, gas holdup, flow patterns, Sauter mean bubble diameter, and volumetric mass transfer coefficient were investigated for three different types of impeller using a multiple reference frame (MRF) model within the whole multiphase bioreactor. The effects of aeration rate and impeller speed on gas holdup and volumetric mass transfer coefficient were investigated owing to oxygen limitation in high cell density cultivation (HCDC). As high viscosity puts a limit on the efficiency of the bioreactor, the influences of viscosity on Sauter mean diameter, gas holdup, and volumetric mass transfer coefficient were also assessed. To determine growth kinetics as well as gas holdup, a set of experiments was performed. The numerical results of gas holdup and kLa were compared with the experimental data. Obtained results suggest that the Scaba impeller results in higher values of volumetric mass transfer coefficient, and subsequently higher biomass concentrations. One of the greatest problems in HCDC is feed accumulation in particular places. As depletion of substrate occurs near the impeller, the best spot for feeding purposes is in the vicinity of the impeller. Current research gives insight into the determination of the optimal operating conditions of HCDC in stirred bioreactors.

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“…For the inner region the continuity and the momentum equations are solved in rotating framework as well as agitator and for the outer region those equations are solved in the stationary framework as well as mesh filter [27]. The assumptions required for CFD simulation using Fluent 6.3.26 software and the numerical implementation has been explained in detail at the study which has been previously done [28].…”

Section: Cfd Simulationmentioning

confidence: 99%

Formation of pre-coating dynamic membrane on mesh filter by cross-flow filtration of PAC–water suspension in a bioreactor: experimental and modeling

Poostchi¹,

Bayat²,

Rezaei³

et al. 2015

Desalination and Water Treatment

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A B S T R A C TThis study presents a closed-loop process for forming pre-coated dynamic membrane used in membrane bioreactor. Powder-activated carbon (PAC) particles are suspended by using axial-flow agitator whilst simultaneously deposited upon the filter medium by cross-flow filtration. The revolution speed of agitator is varied in the range of 150-450 rpm. Computational fluid dynamic is used to calculate velocity and shear rate adjacent to membrane surface. The force balance of the particles considers the driving force of dynamic membrane formation, drag force due to negative pressure-driven of suction pump, lift force due to mechanical mixing, and adhesive force due to particle-particle interactions. The effect of two major terms including drag and lift forces on the porosity of dynamic membrane is systematically investigated. The lower drag force results in the higher porosity of dynamic membrane. The results also show that the denser dynamic membrane is formed when the lift force becomes lower. The porosity of dynamic membrane is ranged from 0.38 to 0.46 while the thickness of dynamic membrane is altered between 100 and 500 μm. A dimensionless immobilized parameter is derived to predict the formation of pre-coating dynamic membrane. The results obtained from modeling show that dynamic membranes formed at higher values of immobilization parameter have a more cohesive structure. The results obtained from the experiments and the model reveal that the thickness of 400 μm of PAC layer is considered as a stability threshold thereafter the structure of dynamic membrane becomes loose and unstable. The rejection capacity of dynamic membrane is also evaluated at different thicknesses by using Formazin solution. The results reveal that the turbidity of filtrate decreases with increasing the thickness of dynamic membrane. The results display that rejection capacity of pre-coating dynamic membrane is comparable to that of microfiltration membrane.

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Experimental Study and Computational Fluid Dynamics Simulation of a Full-Scale Membrane Bioreactor for Municipal Wastewater Treatment Application

Cited by 38 publications

References 25 publications

Hydraulic optimization of membrane bioreactor via baffle modification using computational fluid dynamics

Hydraulic optimization of membrane bioreactor via baffle modification using computational fluid dynamics

Experiments and a three‐phase computational fluid dynamics (CFD) simulation coupled with population balance equations of a stirred tank bioreactor for high cell density cultivation

Formation of pre-coating dynamic membrane on mesh filter by cross-flow filtration of PAC–water suspension in a bioreactor: experimental and modeling

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