A Steady-State Biofilm Model for Simultaneous Reduction of Nitrate and Perchlorate, Part 1: Model Development and Numerical Solution

Tang, Youneng; Zhao, He-Ping; Marcus, Andrew K.; Krajmalnik‐Brown, Rosa; Rittmann, Bruce E.

doi:10.1021/es203129s

Cited by 48 publications

(35 citation statements)

References 47 publications

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“…Biofilm shrinkage is considered in biofilm models based on the continuous method [e.g., Wanner and Gujer , ; Rittmann and Manem , ; Alpkvist et al ., ; Tang et al ., ], but is neglected in most biofilm models based on the discrete method (i.e., CAM) [e.g., Picioreanu et al ., ; Noguera et al ., ; Hermanowicz , ; Knutson et al ., ]. Neglecting biofilm shrinkage in most previous CAM‐based biofilm models is acceptable as the biofilm was expanding during the simulation or flow was neglected.…”

Section: Introductionmentioning

confidence: 99%

An improved pore-scale biofilm model and comparison with a microfluidic flow cell experiment

et al. 2013

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[1] This work presents a pore-scale biofilm model that solves the flow field using the lattice Boltzmann method, the concentration field of chemical species using the finite difference method, and biofilm development using the cellular automaton method. We adapt the model from a previous work and expand it by implementing biofilm shrinkage in the cellular automaton method. The new pore-scale biofilm model is then evaluated against a previously published pore-scale biofilm experiment, in which two microfluidic flow cells, one with a homogeneous pore network and the other with an aggregate pore network, were tested for aerobic degradation of a herbicide. The simulated biofilm distribution and morphology, biomass accumulation, and contaminant removal are generally consistent with the experimental data. Biofilm detachment in this model occurs when the local shear stress is above a critical value. We use the critical value from our previously published modeling study and find it works well in this case, even though we now have a different pore network and a different microbial species. We also use the model to show that the interaction between flow and biofilm growth is important to predict contaminant removal. The computational time of the new model is reduced 90% compared to our prior work due to implementation of biofilm shrinkage in the cellular automaton method. To the best of our knowledge, this is the first time that biofilm shrinkage has been incorporated into a pore-scale model for simulation of pollutant biodegradation in porous media.

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Section: Introductionmentioning

confidence: 99%

An improved pore-scale biofilm model and comparison with a microfluidic flow cell experiment

et al. 2013

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“…Processes and expressions for their reaction kinetics are summarized in Table . The numerical solution method for this model is the same as that in Tang, Zhao, Marcus, Krajmalnik‐Brown, and Rittmann ().…”

Section: Methodsmentioning

confidence: 99%

“…The boundary condition at the interface of the water diffusion layer and biofilm ( z = L f ) for nitrate and methane is shown in Equation (i.e., continuity in flux), where S bi is calculated with Equation . Derivation of Equations and can be found in Tang, Zhao, et al ().

\frac{D_{i}}{L_{d}} false(S_{b i} goodbreakinfix- {S_{f i} true|}_{z = L_{f}} false) goodbreakinfix= D_{f i} {\frac{d S_{f i}}{d z} true|}_{z = L_{f}} false(i goodbreakinfix= 1 goodbreakinfix, 2 false)

S_{b i} goodbreakinfix= \frac{S_{i} \frac{Q}{A} + {S_{f i} true|}_{z = L_{f}} \frac{D_{i}}{L_{d}}}{false(\frac{D_{i}}{L_{d}} + \frac{Q}{A} false)} false(i goodbreakinfix= 1 goodbreakinfix, 2 false)

…”

Section: Methodsmentioning

confidence: 99%

“…The fraction variation of solid biomass species in the biofilm is governed by Equation (i.e., mass balance on each solid biomass species), where the average specific growth rate of all solid biomass species (

\overset{μ}{true}

) is calculated by Equation , and the moving speed of the biofilm at position z ( u L ) is calculated by Equation . Derivation of Equations – can be found in Tang, Zhao, et al ().

\frac{d f_{j}}{d t} goodbreakinfix= false(μ_{j} goodbreakinfix- \overset{μ}{true} false) f_{j} goodbreakinfix- u_{L} \frac{d f_{j}}{d z} goodbreakinfix= 0 false(j goodbreakinfix= 1 goodbreakinfix, 2 goodbreakinfix, 3 false)

\overset{μ}{true} goodbreakinfix= \sum_{j = 1}^{j = 3} (μ_{j} f_{j} false)

u_{L} goodbreakinfix= \frac{d L}{d t} goodbreakinfix= \int_{0}^{z} \sum_{j = 1}^{j = 3} false(μ_{j} f_{j} false) d z goodbreakinfix- k_{det} goodbreakinfix\times {L_{f}}^{2} goodbreakinfix\times \frac{L}{L_{f}}

…”

Section: Methodsmentioning

confidence: 99%

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Kinetics of anaerobic methane oxidation coupled to denitrification in the membrane biofilm reactor

Tang

Zhang

Rittmann

et al. 2019

Biotech & Bioengineering

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Anaerobic oxidation of methane coupled to denitrification (AOM-D) in a membrane biofilm reactor (MBfR), a platform used for efficiently coupling gas delivery and biofilm development, has attracted attention in recent years due to the low cost and high availability of methane. However, experimental studies have shown that the nitrate-removal flux in the CH 4 -based MBfR (<1.0 g N/m 2 -day) is about one order of magnitude smaller than that in the H 2 -based MBfR (1.1-6.7 g N/m 2 -day). A onedimensional multispecies biofilm model predicts that the nitrate-removal flux in the CH 4 -based MBfR is limited to <1.7 g N/m 2 -day, consistent with the experimental studies reported in the literature. The model also determines the two major limiting factors for the nitrate-removal flux: The methane half-maximum-rate concentration (K 2 ) and the specific maximum methane utilization rate of the AOM-D syntrophic consortium (k max2 ), with k max2 being more important. Model simulations show that increasing k max2 to >3 g chemical oxygen demand (COD)/g cell-day (from its current 1.8 g COD/g cell-day) and developing a new membrane with doubled methanedelivery capacity (D m ) could bring the nitrate-removal flux to ≥4.0 g N/m 2 -day, which is close to the nitrate-removal flux for the H 2 -based MBfR. Further increase of the maximum nitrate-removal flux can be achieved when D m and k max2 increase together.

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“…Mathematical models have been widely used to predict nitrate and nitrite dynamics during hydrogenotrophic denitrification (Martin, Picioreanu, & Nerenberg, ; Tang, Zhao, Marcus, Krajmalnik‐Brown, & Rittmann, ,b; Tang, Zhou, Ziv‐El, & Rittmann, ). In contrast, little effort has been dedicated to modeling the N 2 O dynamics during hydrogenotrophic denitrification despite of considerable amounts of N 2 O accumulation in this process and its detrimental impact on the atmosphere (Li et al, ).…”

Section: Introductionmentioning

confidence: 99%

Modeling electron competition among nitrogen oxides reduction and N₂O accumulation in hydrogenotrophic denitrification

Liu

Ngo

Guo

et al. 2018

Biotech & Bioengineering

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Hydrogenotrophic denitrification is a novel and sustainable process for nitrogen removal, which utilizes hydrogen as electron donor, and carbon dioxide as carbon source. Recent studies have shown that nitrous oxide (N O), a highly undesirable intermediate and potent greenhouse gas, can accumulate during this process. In this work, a new mathematical model is developed to describe nitrogen oxides dynamics, especially N O, during hydrogenotrophic denitrification for the first time. The model describes electron competition among the four steps of hydrogenotrophic denitrification through decoupling hydrogen oxidation and nitrogen reduction processes using electron carriers, in contrast to the existing models that couple these two processes and also do not consider N O accumulation. The developed model satisfactorily describes experimental data on nitrogen oxides dynamics obtained from two independent hydrogenotrophic denitrifying cultures under various hydrogen and nitrogen oxides supplying conditions, suggesting the validity and applicability of the model. The results indicated that N O accumulation would not be intensified under hydrogen limiting conditions, due to the higher electron competition capacity of N O reduction in comparison to nitrate and nitrite reduction during hydrogenotrophic denitrification. The model is expected to enhance our understanding of the process during hydrogenotrophic denitrification and the ability to predict N O accumulation.

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A Steady-State Biofilm Model for Simultaneous Reduction of Nitrate and Perchlorate, Part 1: Model Development and Numerical Solution

Cited by 48 publications

References 47 publications

An improved pore-scale biofilm model and comparison with a microfluidic flow cell experiment

An improved pore-scale biofilm model and comparison with a microfluidic flow cell experiment

Kinetics of anaerobic methane oxidation coupled to denitrification in the membrane biofilm reactor

Modeling electron competition among nitrogen oxides reduction and N₂O accumulation in hydrogenotrophic denitrification

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A Steady-State Biofilm Model for Simultaneous Reduction of Nitrate and Perchlorate, Part 1: Model Development and Numerical Solution

Cited by 48 publications

References 47 publications

An improved pore-scale biofilm model and comparison with a microfluidic flow cell experiment

An improved pore-scale biofilm model and comparison with a microfluidic flow cell experiment

Kinetics of anaerobic methane oxidation coupled to denitrification in the membrane biofilm reactor

Modeling electron competition among nitrogen oxides reduction and N2O accumulation in hydrogenotrophic denitrification

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Modeling electron competition among nitrogen oxides reduction and N₂O accumulation in hydrogenotrophic denitrification